139 results on '"Duan, Ling-Yu"'
Search Results
2. Transfer metric learning: algorithms, applications and outlooks
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Luo, Yong, Wen, Yonggang, Hu, Han, Du, Bo, Duan, Ling-Yu, and Tao, Dacheng
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- 2024
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3. Face Image Reflection Removal
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Wan, Renjie, Shi, Boxin, Li, Haoliang, Duan, Ling-Yu, and Kot, Alex C.
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- 2021
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4. IDeRs: Iterative dehazing method for single remote sensing image
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Xu, Long, Zhao, Dong, Yan, Yihua, Kwong, Sam, Chen, Jie, and Duan, Ling-Yu
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- 2019
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5. Multi-scale Optimal Fusion model for single image dehazing
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Zhao, Dong, Xu, Long, Yan, Yihua, Chen, Jie, and Duan, Ling-Yu
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- 2019
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6. DeepShoe: An improved Multi-Task View-invariant CNN for street-to-shop shoe retrieval
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Zhan, Huijing, Shi, Boxin, Duan, Ling-Yu, and Kot, Alex C.
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- 2019
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7. mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
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Li, Xiaotong, Ge, Yixiao, Yi, Kun, Hu, Zixuan, Shan, Ying, and Duan, Ling-Yu
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing the continuous visual signals into discrete vision tokens using a pre-learned dVAE. Despite a feasible solution, the improper discretization hinders further improvements of image pre-training. Since image discretization has no ground-truth answers, we believe that the masked patch should not be assigned with a unique token id even if a better tokenizer can be obtained. In this work, we introduce an improved BERT-style image pre-training method, namely mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice training objectives. Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image tokenizer and further refined by high-level inter-patch perceptions resorting to the observation that similar patches should share their choices. Extensive experiments on classification, segmentation, and detection tasks demonstrate the superiority of our method, e.g., the pre-trained ViT-B achieves 84.1% top-1 fine-tuning accuracy on ImageNet-1K classification, 49.2% AP^b and 44.0% AP^m of object detection and instance segmentation on COCO, 50.8% mIOU on ADE20K semantic segmentation, outperforming the competitive counterparts. The code will be available at https://github.com/lixiaotong97/mc-BEiT., Accepted by ECCV 2022
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- 2022
8. Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains
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Dai, Yongxing, Sun, Yifan, Liu, Jun, Tong, Zekun, Yang, Yi, and Duan, Ling-Yu
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Cross-domain person re-identification (re-ID), such as unsupervised domain adaptive (UDA) re-ID, aims to transfer the identity-discriminative knowledge from the source to the target domain. Existing methods commonly consider the source and target domains are isolated from each other, i.e., no intermediate status is modeled between both domains. Directly transferring the knowledge between two isolated domains can be very difficult, especially when the domain gap is large. From a novel perspective, we assume these two domains are not completely isolated, but can be connected through intermediate domains. Instead of directly aligning the source and target domains against each other, we propose to align the source and target domains against their intermediate domains for a smooth knowledge transfer. To discover and utilize these intermediate domains, we propose an Intermediate Domain Module (IDM) and a Mirrors Generation Module (MGM). IDM has two functions: 1) it generates multiple intermediate domains by mixing the hidden-layer features from source and target domains and 2) it dynamically reduces the domain gap between the source / target domain features and the intermediate domain features. While IDM achieves good domain alignment, it introduces a side effect, i.e., the mix-up operation may mix the identities into a new identity and lose the original identities. To compensate this, MGM is introduced by mapping the features into the IDM-generated intermediate domains without changing their original identity. It allows to focus on minimizing domain variations to promote the alignment between the source / target domain and intermediate domains, which reinforces IDM into IDM++. We extensively evaluate our method under both the UDA and domain generalization (DG) scenarios and observe that IDM++ yields consistent performance improvement for cross-domain re-ID, achieving new state of the art., This work is a journal extension of our ICCV 2021 Oral paper https://openaccess.thecvf.com/content/ICCV2021/html/Dai_IDM_An_Intermediate_Domain_Module_for_Domain_Adaptive_Person_Re-ID_ICCV_2021_paper.html
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- 2022
9. Learning from mobile contexts to minimize the mobile location search latency
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Duan, Ling-Yu, Ji, Rongrong, Chen, Jie, Yao, Hongxun, Huang, Tiejun, and Gao, Wen
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- 2013
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10. Uncertainty Modeling for Out-of-Distribution Generalization
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Li, Xiaotong, Dai, Yongxing, Ge, Yixiao, Liu, Jun, Shan, Ying, and Duan, Ling-Yu
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (cs.LG) - Abstract
Though remarkable progress has been achieved in various vision tasks, deep neural networks still suffer obvious performance degradation when tested in out-of-distribution scenarios. We argue that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features and do not explicitly consider the uncertain statistics discrepancy caused by potential domain shifts during testing. In this paper, we improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. Hence, each feature statistic is no longer a deterministic value, but a probabilistic point with diverse distribution possibilities. With the uncertain feature statistics, the models can be trained to alleviate the domain perturbations and achieve better robustness against potential domain shifts. Our method can be readily integrated into networks without additional parameters. Extensive experiments demonstrate that our proposed method consistently improves the network generalization ability on multiple vision tasks, including image classification, semantic segmentation, and instance retrieval. The code can be available at https://github.com/lixiaotong97/DSU., Accepted by ICLR 2022
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- 2022
11. Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning.
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ZHANG, LIN, FAN, LIXIN, LUO, YONG, and DUAN, LING-YU
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ARTIFICIAL intelligence ,DATA distribution ,COMPUTATIONAL complexity ,ACQUISITION of data - Abstract
The rapid development of modern artificial intelligence technique is mainly attributed to sufficient and high-quality data. However, in the data collection, personal privacy is at risk of being leaked. This issue can be addressed by federated learning, which is proposed to achieve efficient model training among multiple data providers without direct data access and aggregation. To encourage more parties owning high-quality data to participate in the federated learning, it is important to evaluate and reward the participant contribution in a reasonable, robust, and efficient manner. To achieve this goal, we propose a novel contribution estimation method: Intrinsic Performance Influence-based Contribution Estimation (IPICE). In particular, the class-level intrinsic performance influence is adopted as the contribution estimation criteria in IPICE, and a neural network is employed to exploit the non-linear relationship between the performance change and estimated contribution. Extensive experiments are conducted on various datasets, and the results demonstrate that IPICE is more accurate and stable than the counterpart in various data distribution settings. The computational complexity is significantly reduced in our IPICE, especially when a new party joins the federation. IPICE assigns small contributions to bad/garbage data and thus prevent them from participating and deteriorating the learning ecosystem. [ABSTRACT FROM AUTHOR]
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- 2022
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12. Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics
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Yang, Wenhan, Huang, Haofeng, Hu, Yueyu, Duan, Ling-Yu, and Liu, Jiaying
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. In this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks., The first three authors had equal contribution. arXiv admin note: text overlap with arXiv:2106.08512
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- 2021
13. Interactive ads recommendation with contextual search on product topic space
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Wang, Jinqiao, Wang, Bo, Duan, Ling-yu, Tian, Qi, and Lu, Hanqing
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- 2014
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14. Disentangled Feature Learning Network and a Comprehensive Benchmark for Vehicle Re-Identification.
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Bai, Yan, Liu, Jun, Lou, Yihang, Wang, Ce, and Duan, Ling-Yu
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VEHICLE models ,IMAGE registration ,WEATHER ,PUBLIC safety ,VEHICLES ,AUTOMOBILE license plates - Abstract
Vehicle Re-Identification (ReID) is of great significance for public security and intelligent transportation. Large and comprehensive datasets are crucial for the development of vehicle ReID in model training and evaluation. However, existing datasets in this field have limitations in many aspects, including the constrained capture conditions, limited variation of vehicle appearances, and small scale of training and test set, etc. Hence, a new, large, and challenging benchmark for vehicle ReID is urgently needed. In this paper, we propose a large vehicle ReID dataset, called VERI-Wild 2.0, containing 825,042 images. It is captured using a city-scale surveillance camera system, consisting of 274 cameras covering a very large area over 200 $km^2$ k m 2 . Specifically, the samples in our dataset present very rich appearance diversities thanks to the long time span collecting settings, unconstrained capturing viewpoints, various illumination conditions, diversified background environments, and different weather conditions. Furthermore, to facilitate more practical benchmarking, we define a challenging and large test set containing about 400K vehicle images that do not have any camera overlap with the training set. VERI-Wild 2.0 is expected to be able to facilitate the design, adaptation, development, and evaluation of different types of learning models for vehicle ReID. Besides, we also design a new method for vehicle ReID. We observe that orientation is a crucial factor for feature matching in vehicle ReID. To match vehicle pairs captured from similar orientations, the learned features are expected to capture specific detailed differential information for discriminating the visually similar yet different vehicles. In contrast, features are desired to capture the orientation invariant common information when matching samples captured from different orientations. Thus a novel disentangled feature learning network (DFNet) is proposed. It explicitly considers the orientation information for vehicle ReID, and concurrently learns the orientation specific and orientation common features that thus can be adaptively exploited via an adaptive matching scheme when dealing with matching pairs from similar or different orientations. The comprehensive experimental results show the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]
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- 2022
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15. Towards Low Light Enhancement With RAW Images.
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Huang, Haofeng, Yang, Wenhan, Hu, Yueyu, Liu, Jiaying, and Duan, Ling-Yu
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IMAGE intensifiers ,IMAGE processing ,METADATA ,DEEP learning - Abstract
In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way. Inspired by a full consideration on the typical image processing pipeline, we are inspired to develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors and provides a tool for exploring how properties of RAW images affect the enhancement performance empirically. The empirical benchmark results show that the Linearity of data and Exposure Time recorded in meta-data play the most critical role, which brings distinct performance gains in various measures over the approaches taking the sRGB images as input. With the insights obtained from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications in a way of using RAW images only in the training phase. REENet projects sRGB images into linear RAW domains to apply constraints with corresponding RAW images to reduce the difficulty of modeling training. After that, in the testing phase, our REENet does not rely on RAW images. Experimental results demonstrate not only the superiority of REENet to state-of-the-art sRGB-based methods and but also the effectiveness of the RAW guidance and all components. [ABSTRACT FROM AUTHOR]
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- 2022
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16. Location Discriminative Vocabulary Coding for Mobile Landmark Search
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Ji, Rongrong, Duan, Ling-Yu, Chen, Jie, Yao, Hongxun, Yuan, Junsong, Rui, Yong, and Gao, Wen
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- 2012
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17. Per-Sample Multiple Kernel Approach for Visual Concept Learning
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Yang, Jingjing, Li, Yuanning, Tian, Yonghong, Duan, Ling-Yu, and Gao, Wen
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- 2010
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18. Learning compact visual descriptors for low bit rate mobile landmark search
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Duan, Ling-Yu, Chen, Jie, Ji, Rongrong, Huang, Tiejun, and Gao, Wen
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Mobile devices -- Usage ,Search theory ,Image processing -- Research ,Query processing -- Research ,Business - Abstract
Along with the ever-growing computational power of mobile devices, mobile visual search has undergone an evolution in techniques and applications. A significant trend is low bit rate visual search, where compact visual descriptors are extracted directly over a mobile and delivered as queries rather than raw images to reduce the query transmission latency. In this article, we introduce our work on low bit rate mobile landmark search, in which a compact yet discriminative landmark image descriptor is extracted by using a location context such as GPS, crowd-sourced hotspot WLAN, and cell tower locations. The compactness originates from the bag-of-words image representation, with offline learning from geotagged photos from online photo-sharing websites including Flickr and Panoramio. The learning process involves segmenting the landmark photo collection by discrete geographical regions using a Gaussian mixture model and then boosting a ranking-sensitive vocabulary within each region, with 'entropy'-based feedback on the compactness of the descriptor to refine both phases iteratively. In online search, when entering a geographical region, the code book in a mobile device is downstream adapted to generate extremely compact descriptors with promising discriminative ability. We have deployed landmark search apps to both HTC and iPhone mobile phones, accessing a database of a million scale images in typical areas like Beijing, New York, and Barcelona, and others. Our descriptor outperforms alternative compact descriptors (Chen et al. 2009; Chen et al., 2010; Chandrasekhar et al. 2009a; Chandrasekhar et al. 2009b) by significant margins. Beyond landmark search, this article will summarize the MPEG standarization progress of compact descriptor for visual search (CDVS) (Yuri et al. 2010; Yuri et al. 2011) toward application interoperability., ********** In recent years, mobile devices, such as smart camera phones and tablet PCs, have shown great potential for visual search, thanks to the integrated functionality of high-resolution embedded cameras, [...]
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- 2013
19. Per-Sample Multiple Kernel Approach for Visual Concept Learning
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Tian Yonghong, Duan Ling-Yu, Yang Jingjing, Li Yuanning, and Gao Wen
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Electronics ,TK7800-8360 - Abstract
Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL) methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL) approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.
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- 2010
20. CoRRN: Cooperative Reflection Removal Network.
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Wan, Renjie, Shi, Boxin, Li, Haoliang, Duan, Ling-Yu, Tan, Ah-Hwee, and Kot, Alex C.
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REFLECTIONS ,APPLICATION software ,FEATURE extraction ,DEEP learning - Abstract
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose a network with the feature-sharing strategy to tackle this problem in a cooperative and unified framework, by integrating image context information and the multi-scale gradient information. To remove the strong reflections existed in some local regions, we propose a statistic loss by considering the gradient level statistics between the background and reflections. Our network is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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- 2020
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21. Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm.
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Lou, Yihang, Duan, Ling-Yu, Luo, Yong, Chen, Ziqian, Liu, Tongliang, Wang, Shiqi, and Gao, Wen
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The digital retina excels at providing enhanced visual sensing and analysis capability for city brain in smart cities, and can feasibly convert the visual data from visual sensors into semantic features. With the deployment of deep learning or handcrafted models, these features are extracted on front-end devices, then delivered to back-end servers for advanced analysis. In this scenario, we propose a model generation, utilization and communication paradigm, aiming at strong front-end sensing capabilities for establishing better artificial visual systems in smart cities. In particular, we propose an integrated multiple deep learning models reuse and prediction strategy, which dramatically increases the feasibility of the digital retina in large-scale visual data analysis in smart cities. The proposed multi-model reuse scheme aims to reuse the knowledge from models cached and transmitted in digital retina to obtain more discriminative capability. To efficiently deliver these newly generated models, a model prediction scheme is further proposed by encoding and reconstructing model differences. Extensive experiments have been conducted to demonstrate the effectiveness of proposed model-centric paradigm. [ABSTRACT FROM AUTHOR]
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- 2020
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22. Skeleton-Based Online Action Prediction Using Scale Selection Network.
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Liu, Jun, Shahroudy, Amir, Wang, Gang, Duan, Ling-Yu, and Kot, Alex C.
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FORECASTING ,MATHEMATICAL convolutions ,SKELETON - Abstract
Action prediction is to recognize the class label of an ongoing activity when only a part of it is observed. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis. Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step. An activation sharing scheme is also proposed to handle the overlapping computations among the adjacent time steps, which enables our framework to run more efficiently. Moreover, to enhance the performance of our framework for action prediction with the skeletal input data, a hierarchy of dilated tree convolutions are also designed to learn the multi-level structured semantic representations over the skeleton joints at each frame. Our proposed approach is evaluated on four challenging datasets. The extensive experiments demonstrate the effectiveness of our method for skeleton-based online action prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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23. Deep Variational and Structural Hashing.
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Liong, Venice Erin, Lu, Jiwen, Duan, Ling-Yu, and Tan, Yap-Peng
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HASHING ,BINARY codes ,MATROIDS ,MATHEMATICAL convolutions - Abstract
In this paper, we propose a deep variational and structural hashing (DVStH) method to learn compact binary codes for multimedia retrieval. Unlike most existing deep hashing methods which use a series of convolution and fully-connected layers to learn binary features, we develop a probabilistic framework to infer latent feature representation inside the network. Then, we design a struct layer rather than a bottleneck hash layer, to obtain binary codes through a simple encoding procedure. By doing these, we are able to obtain binary codes discriminatively and generatively. To make it applicable to cross-modal scalable multimedia retrieval, we extend our method to a cross-modal deep variational and structural hashing (CM-DVStH). We design a deep fusion network with a struct layer to maximize the correlation between image-text input pairs during the training stage so that a unified binary vector can be obtained. We then design modality-specific hashing networks to handle the out-of-sample extension scenario. Specifically, we train a network for each modality which outputs a latent representation that is as close as possible to the binary codes which are inferred from the fusion network. Experimental results on five benchmark datasets are presented to show the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]
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- 2020
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24. Feature Boosting Network For 3D Pose Estimation.
- Author
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Liu, Jun, Ding, Henghui, Shahroudy, Amir, Duan, Ling-Yu, Jiang, Xudong, Wang, Gang, and Kot, Alex C.
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GRAPHICAL modeling (Statistics) ,LOGIC circuits ,TASK performance ,TASK analysis - Abstract
In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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25. Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image.
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Zhao, Dong, Xu, Long, Chen, Linjie, Yan, Yihua, and Duan, Ling-Yu
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SOLAR flares ,DEEP learning ,HELIOSEISMOLOGY ,ART & state ,IMAGING systems ,VERY large array telescopes ,IMAGE reconstruction - Abstract
Overexposure may happen for imaging of solar observation as extremely violet solar bursts occur, which means that signal intensity goes beyond the dynamic range of imaging system of a telescope, resulting in loss of signal. For example, during solar flare, Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) often records overexposed images/videos, resulting loss of fine structures of solar flare. This paper makes effort to retrieve/recover missing information of overexposure by exploiting deep learning for its powerful nonlinear representation which makes it widely used in image reconstruction/restoration. First, a new model, namely, mask-Pix2Pix network, is proposed for overexposure recovery. It is built on a well-known Pix2Pix network of conditional generative adversarial network (cGAN). In addition, a hybrid loss function, including an adversarial loss, a masked L1 loss and a edge mass loss/smoothness, are integrated together for addressing challenges of overexposure relative to conventional image restoration. Moreover, a new database of overexposure is established for training the proposed model. Extensive experimental results demonstrate that the proposed mask-Pix2Pix network can well recover missing information of overexposure and outperforms the state of the arts originally designed for image reconstruction tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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26. Embedding Adversarial Learning for Vehicle Re-Identification.
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Lou, Yihang, Bai, Yan, Liu, Jun, Wang, Shiqi, and Duan, Ling-Yu
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GALLIUM nitride ,VEHICLES ,IN-vehicle computing - Abstract
The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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27. Front-End Smart Visual Sensing and Back-End Intelligent Analysis: A Unified Infrastructure for Economizing the Visual System of City Brain.
- Author
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Lou, Yihang, Duan, Ling-Yu, Wang, Shiqi, Chen, Ziqian, Bai, Yan, Chen, Changwen, and Gao, Wen
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SMART cities ,BRAIN ,ELECTRONIC surveillance ,DEEP learning - Abstract
The visual data, which are acquired from the ubiquitous visual sensors deployed in metropolitans, are of great value and paramount significance to enhance the effectiveness and pursue the future development of smart cities. In this paper, the essential building blocks of the unified visual data management and analysis infrastructure that serve as the foundation for the economical visual system in the city brain, are introduced to facilitate the utilization of the visual signal in the artificial intelligence era. In particular, we start by the discussion of the front-end smart visual sensing in the context of economical communication and service with the heterogeneous network, and the functionalities and necessities of compact visual feature and deep learning model representations are detailed. Subsequently, the utilities of the infrastructure are demonstrated through two intelligent applications at the back-end, including vehicle re-identification and person re-identification. The standardizations regarding compact feature and deep neural network representations, which are regarded as the key ingredients in this infrastructure and greatly facilitate the construction of the visual system in the city brain, are also discussed. Finally, we envision how the potential issues regarding the economical visual communications for future smart cities might be pragmatically approached within this unified infrastructure. [ABSTRACT FROM AUTHOR]
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- 2019
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28. Toward Knowledge as a Service Over Networks: A Deep Learning Model Communication Paradigm.
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Chen, Ziqian, Duan, Ling-Yu, Wang, Shiqi, Lou, Yihang, Huang, Tiejun, Wu, Dapeng Oliver, and Gao, Wen
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DEEP learning ,COMMUNICATION models ,INTELLIGENCE service ,ARTIFICIAL intelligence ,INTERNET of things ,BIG data - Abstract
The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model communication paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model communication through a set of experiments. Moreover, the interoperability in deep learning model communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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29. Compact Descriptors for Video Analysis: The Emerging MPEG Standard.
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Duan, Ling-Yu, Lou, Yihang, Bai, Yan, Huang, Tiejun, Gao, Wen, Chandrasekhar, Vijay, Lin, Jie, Wang, Shiqi, and Kot, Alex Chichung
- Subjects
MPEG (Video coding standard) ,DESCRIPTOR systems ,VIDEOS - Abstract
This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG). MPEG-CDVA targets at defining a standardized bitstream syntax to enable interoperability in the context of video analysis applications. During the developments of MPEG-CDVA, a series of techniques aiming to reduce the descriptor size and improve the video representation ability have been proposed. This paper describes the new standard that is being developed and reports the performance of these key technical contributions. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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30. Unified Spatio-Temporal Attention Networks for Action Recognition in Videos.
- Author
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Li, Dong, Yao, Ting, Duan, Ling-Yu, Mei, Tao, and Rui, Yong
- Abstract
Recognizing actions in videos is not a trivial task because video is an information-intensive media and includes multiple modalities. Moreover, on each modality, an action may only appear at some spatial regions, or only part of the temporal video segments may contain the action. A valid question is how to locate the attended spatial areas and selective video segments for action recognition. In this paper, we devise a general attention neural cell, called AttCell, that estimates the attention probability not only at each spatial location but also for each video segment in a temporal sequence. With AttCell, a unified Spatio-Temporal Attention Networks (STAN) is proposed in the context of multiple modalities. Specifically, STAN extracts the feature map of one convolutional layer as the local descriptors on each modality and pools the extracted descriptors with the spatial attention measured by AttCell as a representation of each segment. Then, we concatenate the representation on each modality to seek a consensus on the temporal attention, a priori, to holistically fuse the combined representation of video segments to the video representation for recognition. Our model differs from conventional deep networks, which focus on the attention mechanism, because our temporal attention provides a principled and global guidance across different modalities and video segments. Extensive experiments are conducted on four public datasets; UCF101, CCV, THUMOS14, and Sports-1M; our STAN consistently achieves superior results over several state-of-the-art techniques. More remarkably, we validate and demonstrate the effectiveness of our proposal when capitalizing on the different number of modalities. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
31. Codebook-Free Compact Descriptor for Scalable Visual Search.
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Wu, Yuwei, Gao, Feng, Huang, Yicheng, Lin, Jie, Chandrasekhar, Vijay, Yuan, Junsong, and Duan, Ling-Yu
- Abstract
The MPEG compact descriptors for visual search (CDVS) is a standard toward image matching and retrieval. To achieve high retrieval accuracy over a large scale image/video dataset, recent research efforts have demonstrated that employing extremely high-dimensional descriptors such as the Fisher vector (FV) and the vector of locally aggregated descriptors (VLAD) can yield good performance. Since the FV (or VLAD) possesses high discriminability but small visual vocabulary, it has been adopted by CDVS to construct a global compact descriptor. In this paper, we study the development of global compact descriptors in the completed CDVS standard and the emerging compact descriptors for video analysis (CDVA) standard, in which we formulate the FV (or VLAD) compression as a resource-constrained optimization problem. Accordingly, we propose a codebook-free aggregation method via dual selection to generate a global compact visual descriptor, which supports fast and accurate feature matching free of large visual codebooks, fulfilling the low memory requirement of mobile visual search at significantly reduced latency. Specifically, we investigate both sample-specific Gaussian component redundancy and bit dependency within a binary aggregated descriptor to produce compact binary codes. Our technique contributes to the scalable compressed Fisher vector (SCFV) adopted by the CDVS standard. Moreover, the SCFV descriptor is currently serving as the frame-level hand-crafted video feature, which inspires the inheritance of CDVS descriptors for the emerging CDVA standard. Furthermore, we investigate the positive complementary effect of our standard compliant compact descriptor and deep learning based features extracted from convolutional neural networks with significant mean average precision gains. Extensive evaluation over benchmark databases shows the significant merits of the codebook-free binary codes for scalable visual search. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
32. Data-Driven Lightweight Interest Point Selection for Large-Scale Visual Search.
- Author
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Gao, Feng, Zhang, Xinfeng, Huang, Yicheng, Luo, Yong, Li, Xiaoming, and Duan, Ling-Yu
- Abstract
With the explosive increase of images and videos, visual analysis has become an essential technique in dealing with the big visual data, which utilizes the visual feature descriptors to search or recognize the images or frames with target objects or events. Subject to the constraints of resources (e.g., memory, bandwidth, storage, etc.), interest point selection is crucial to generate robust compact descriptors for high-efficiency visual analysis by selecting and aggregating the most discriminative local feature descriptors, which has been demonstrated in the state-of-the-art low bit rate visual search works. In this paper, we propose a data-driven lightweight interest point selection approach to significantly improve the performance of visual search, while ameliorating the efficiency of extracting feature descriptors. Comprehensive experimental results over benchmarks have shown that the proposed interest point selection algorithm has significantly improved image matching and retrieval performance in the completed MPEG Compact Descriptors for Visual Search (CDVS) standard as well as the emerging MPEG Compact Descriptors for Video Analytics (CDVA) standard, say 20% mAP gain by data-driven selection against random selection of interest points. In particular, the presented data-driven interest point selection has been adopted by MPEG-CDVS and MPEG-CDVA as a normative technique to improve the aggregation of handcrafted features, which has contributed to the combination of handcrafted features and deep learning (CNN) features as well. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
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33. Group-Sensitive Triplet Embedding for Vehicle Reidentification.
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Bai, Yan, Lou, Yihang, Gao, Feng, Wang, Shiqi, Wu, Yuwei, and Duan, Ling-Yu
- Abstract
The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation “group” between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database “PKU-Vehicle,” consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
34. Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach.
- Author
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Fu, Qiang, Luo, Yong, Wen, Yonggang, Tao, Dacheng, Li, Ying, and Duan, Ling-Yu
- Abstract
It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In the system, a key component is high-precision product search, which is to fulfill exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modeled and exploited by a simple Euclidean distance. This motivates us to introduce distance metric learning (DML) for improving the distance estimation. However, in traditional DML methods, the side information (such as the similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain is leveraged. These methods may fail due to limited side information. Fortunately, this issue can be alleviated by utilizing transfer metric learning (TML) to exploit information from other related domains. In this paper, a novel manifold regularized heterogeneous multitask metric learning framework is proposed, in which each domain is treated equally. The proposed approach allows us to simultaneously exploit the information from other domains and the unlabeled information. Furthermore, the ranking-based loss is adopted to make our model more appropriate for search. Experiments on two challenging real-world datasets demonstrate the effectiveness of the proposed method. This TML approach is expected to impact the transformation of the emerging T2O trend in both TV and online video domains. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
35. Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing.
- Author
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Duan, Ling-Yu, Sun, Wei, Zhang, Xinfeng, Wang, Shiqi, Chen, Jie, Yin, Jianxiong, See, Simon, Huang, Tiejun, Kot, Alex C., and Gao, Wen
- Subjects
- *
MOTHERBOARDS , *GRAPHICS processing units , *SYNTAX in programming languages , *COMPUTER storage devices , *DATABASE management - Abstract
The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of a CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In this paper, we revisit the merits of low complexity design of CDVS core techniques and present a very fast CDVS encoder by leveraging the massive parallel execution resources of graphics processing unit (GPU). We elegantly shift the computation-intensive and parallel-friendly modules to the state-of-the-arts GPU platforms, in which the thread block allocation as well as the memory access mechanism are jointly optimized to eliminate performance loss. In addition, those operations with heavy data dependence are allocated to CPU for resolving the extra but non-necessary computation burden for GPU. Furthermore, we have demonstrated the proposed fast CDVS encoder can work well with those convolution neural network approaches which enables to leverage the advantages of GPU platforms harmoniously, and yield significant performance improvements. Comprehensive experimental results over benchmarks are evaluated, which has shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising for scalable visual search. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
36. Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks.
- Author
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Liu, Jun, Wang, Gang, Duan, Ling-Yu, Abdiyeva, Kamila, and Kot, Alex C.
- Subjects
HUMAN behavior ,PATTERN recognition systems ,THREE-dimensional imaging ,SHORT-term memory ,PERFORMANCE evaluation - Abstract
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
37. HNIP: Compact Deep Invariant Representations for Video Matching, Localization, and Retrieval.
- Author
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Lin, Jie, Duan, Ling-Yu, Wang, Shiqi, Bai, Yan, Lou, Yihang, Chandrasekhar, Vijay, Huang, Tiejun, Kot, Alex, and Gao, Wen
- Abstract
With emerging demand for large-scale video analysis, MPEG initiated the compact descriptor for video analysis (CDVA) standardization in 2014. Beyond handcrafted descriptors adopted by the current MPEG-CDVA reference model, we study the problem of deep learned global descriptors for video matching, localization, and retrieval. First, inspired by a recent invariance theory, we propose a nested invariance pooling (NIP) method to derive compact deep global descriptors from convolutional neural networks (CNNs), by progressively encoding translation, scale, and rotation invariances into the pooled descriptors. Second, our empirical studies have shown that a sequence of well designed pooling moments (e.g., max or average) may drastically impact video matching performance, which motivates us to design hybrid pooling operations via NIP (HNIP). HNIP has further improved the discriminability of deep global descriptors. Third, the technical merits and performance improvements by combining deep and handcrafted descriptors are provided to better investigate the complementary effects. We evaluate the effectiveness of HNIP within the well-established MPEG-CDVA evaluation framework. The extensive experiments have demonstrated that HNIP outperforms the state-of-the-art deep and canonical handcrafted descriptors with significant mAP gains of 5.5% and 4.7%, respectively. In particular the combination of HNIP incorporated CNN descriptors and handcrafted global descriptors has significantly boosted the performance of CDVA core techniques with comparable descriptor size. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
38. Two-stage pooling of deep convolutional features for image retrieval.
- Author
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Zhi, Tiancheng, Duan, Ling-Yu, Wang, Yitong, and Huang, Tiejun
- Published
- 2016
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- View/download PDF
39. Depth-based local feature selection for mobile visual search.
- Author
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Liu, Zhaoliang, Duan, Ling-Yu, Chen, Jie, and Huang, Tiejun
- Published
- 2016
- Full Text
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40. Overview of the MPEG CDVS Standard.
- Author
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Duan, Ling-Yu, Huang, Tiejun, and Gao, Wen
- Published
- 2015
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- View/download PDF
41. Hierarchical multi-VLAD for image retrieval.
- Author
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Wang, Yitong, Duan, Ling-Yu, Lin, Jie, Wang, Zhe, and Huang, Tiejun
- Published
- 2015
- Full Text
- View/download PDF
42. An efficient coding framework for compact descriptors extracted from video sequence.
- Author
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Huang, Zhangshuai, Duan, Ling-Yu, Lin, Jie, Wang, Shiqi, Ma, Siwei, and Huang, Tiejun
- Published
- 2015
- Full Text
- View/download PDF
43. Query-Adaptive Small Object Search Using Object Proposals and Shape-Aware Descriptors.
- Author
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Bhattacharjee, Sreyasee Das, Yuan, Junsong, Tan, Yap-Peng, and Duan, Ling-Yu
- Abstract
While there has been a significant amount of work on object search and image retrieval, the focus has primarily been on establishing effective models for the whole images, scenes, and objects occupying a large portion of an image. In this paper, we propose to leverage object proposals to identify small and smooth-structured objects in a large image database. Unlike popular methods exploring a coarse image-level pairwise similarity, the search is designed to exploit the similarity measures at the proposal level. An effective graph-based query expansion strategy is designed to assess each of these better matched proposals against all its neighbors within the same image for a precise localization. Combined with a shape-aware feature descriptor EdgeBoW, a set of more insightful edge-weights and node-utility measures, the proposed search strategy can handle varying view angles, illumination conditions, deformation, and occlusion efficiently. Experiments performed on a number of other benchmark datasets show the powerful and superior generalization ability of this single integrated framework in dealing with both clutter-intensive real-life images and poor-quality binary document images at equal dexterity. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
44. Finding the Secret of Image Saliency in the Frequency Domain.
- Author
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Li, Jia, Duan, Ling-Yu, Chen, Xiaowu, Huang, Tiejun, and Tian, Yonghong
- Subjects
- *
DISCRETE Fourier transforms , *IMAGE processing , *IMAGE analysis , *MATHEMATICAL models , *COMPUTER vision , *SUPERVISED learning - Abstract
There are two sides to every story of visual saliency modeling in the frequency domain. On the one hand, image saliency can be effectively estimated by applying simple operations to the frequency spectrum. On the other hand, it is still unclear which part of the frequency spectrum contributes the most to popping-out targets and suppressing distractors. Toward this end, this paper tentatively explores the secret of image saliency in the frequency domain. From the results obtained in several qualitative and quantitative experiments, we find that the secret of visual saliency may mainly hide in the phases of intermediate frequencies. To explain this finding, we reinterpret the concept of discrete Fourier transform from the perspective of template-based contrast computation and thus develop several principles for designing the saliency detector in the frequency domain. Following these principles, we propose a novel approach to design the saliency detector under the assistance of prior knowledge obtained through both unsupervised and supervised learning processes. Experimental results on a public image benchmark show that the learned saliency detector outperforms 18 state-of-the-art approaches in predicting human fixations. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
45. Joint optimization of JPEG quantization table and coefficient thresholding for low bitrate mobile visual search.
- Author
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Wang, Yitong, Duan, Ling-Yu, Lin, Jie, Huang, Tiejun, and Gao, Wen
- Published
- 2014
- Full Text
- View/download PDF
46. Component hashing of variable-length binary aggregated descriptors for fast image search.
- Author
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Wang, Zhe, Duan, Ling-Yu, Lin, Jie, Huang, Tiejun, Gao, Wen, and Bober, Miroslaw
- Published
- 2014
- Full Text
- View/download PDF
47. Weighted Component Hashing of Binary Aggregated Descriptors for Fast Visual Search.
- Author
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Duan, Ling-Yu, Lin, Jie, Wang, Zhe, Huang, Tiejun, and Gao, Wen
- Abstract
Towards low bit rate mobile visual search, recent works have proposed to aggregate the local features and compress the aggregated descriptor (such as Fisher vector, the vector of locally aggregated descriptors) for low latency query delivery as well as moderate search complexity. Even though Hamming distance can be computed very fast, the computational cost of exhaustive linear search over the binary descriptors grows linearly with either the length of a binary descriptor or the number of database images. In this paper, we propose a novel weighted component hashing (WeCoHash) algorithm for long binary aggregated descriptors to significantly improve search efficiency over a large scale image database. Accordingly, the proposed WeCoHash has attempted to address two essential issues in Hashing algorithms: “what to hash” and “how to search.” “What to hash” is tackled by a hybrid approach, which utilizes both image-specific component (i.e., visual word) redundancy and bit dependency within each component of a binary aggregated descriptor to produce discriminative hash values for bucketing. “How to search” is tackled by an adaptive relevance weighting based on the statistics of hash values. Extensive comparison results have shown that WeCoHash is at least 20 times faster than linear search and 10 times faster than local sensitive hash (LSH) when maintaining comparable search accuracy. In particular , the WeCoHash solution has been adopted by the emerging MPEG compact descriptor for visual search (CDVS) standard to significantly speed up the exhaustive search of the binary aggregated descriptors. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
48. A novel pair-wise image matching strategy with compact descriptors.
- Author
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Yang, Shuang, Duan, Ling-Yu, Lin, Jie, and Huang, Tiejun
- Published
- 2013
- Full Text
- View/download PDF
49. On the interoperability of local descriptors compression.
- Author
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Chen, Jie, Duan, Ling-Yu, Lin, Jie, Ji, Rongrong, Huang, Tiejun, and Gao, Wen
- Published
- 2013
- Full Text
- View/download PDF
50. Robust fisher codes for large scale image retrieval.
- Author
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Lin, Jie, Duan, Ling-Yu, Huang, Tiejun, and Gao, Wen
- Published
- 2013
- Full Text
- View/download PDF
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