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We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or “configuration” which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and informative layouts. Moreover, a novel subspace feature weighting technique can be used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization can adapt to the user's (time-varying) notions of content, context and preferences in style and interactive navigation. Monte Carlo simulations with machine-generated layouts as well as pilot user studies have demonstrated the ability of this framework to model or “mimic” users, by automatically generating layouts according to their preferences. [ABSTRACT FROM AUTHOR]

Fine-grained object recognition (FGOR) aims to learn discriminative features that can identify the subtle distinctions between visually similar objects. However, less effort has been devoted to overcoming the impact of object's personalized differences, e.g., varying posture or perspective. We argue that the personalized differences could decline the network's perception of discriminative features, thus discarding some discriminative clues and degrading the FGOR performance accordingly. This motivates us to explore the intrinsic structure knowledge: the fixed spatial correlation between object parts, and thus apply this knowledge to associate diverse semantic parts and recover the missing discriminative details caused by the personalized differences accordingly. In this paper, we propose an end-to-end Structure-driven Relation Graph Network (SRGN) for fine-grained object recognition, and target at exploring and exploiting the object structure information without any additional annotations to associate diverse semantic parts, making the network sensitive to discriminative details influenced by personalized differences. Specifically, the core of SRGN is a Structure-aware Axial Graph (SAG) module, which first infers the structure embedding by establishing the correlation between position information and visual features along the axial direction, and then applies this embedding as aggregation weights to emphasize each discriminative representation by weighted reassembling all relevant features to it. Additionally, our SAG can be readily extensible to a multi-graph schema, that leverages the complementary advantages of different structure embeddings between the position information and visual content, further improving SAG. In this way, our SRGN can demonstrate remarkable robustness in scenarios characterized by extreme distribution perturbations, ultimately leading to superior performance. Extensive experiments and explainable visualizations validate the efficacy of the proposed approach on widely-used fine-grained benchmarks. [ABSTRACT FROM AUTHOR]

Graph convolution networks (GCNs) based methods for 3D human pose estimation usually aggregate immediate features of single-hop nodes, which are unaware of the correlation of multi-hop nodes and therefore neglect long-range dependency for predicting complex poses. In addition, they typically operate either on single-scale or sequential down-sampled multi-scale graph representations, resulting in the loss of contextual information or spatial details. To address these problems, this paper proposes a parallel hop-aware graph attention network (PHGANet) for 3D human pose estimation, which learns enriched hop-aware correlation of the skeleton joints while maintaining the spatially-precise representations of the human graph. Specifically, we propose a hop-aware skeletal graph attention (HSGAT) module to capture the semantic correlation of multi-hop nodes, which first calculates skeleton-based 1-hop attention and then disseminates it to arbitrary hops via graph connectivity. To alleviate the redundant noise introduced by the interactions with distant nodes, HSGAT uses an attenuation strategy to separate attention from distinct hops and assign them learnable attenuation weights according to their distances adaptively. Upon HSGAT, we further build PHGANet with multiple parallel branches of stacked HSGAT modules to learn the enriched hop-aware correlation of human skeletal structures at different scales. In addition, a joint centrality encoding scheme is proposed to introduce node importance as a bias in the learned graph representation, which makes the core joints (e.g., neck and pelvis) more influential during node aggregation. Experimental results indicate that PHGANet performs favorably against state-of-the-art methods on the Human3.6M and MPI-INF-3DHP benchmarks. Models and code are available at https://github.com/ChenyangWang95/PHGANet/. [ABSTRACT FROM AUTHOR]

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for heterogeneous devices including CPU and GPU, by exploiting the redundancy in feature maps, which has rarely been investigated in neural architecture design. For CPU-like devices, we propose a novel CPU-efficient Ghost (C-Ghost) module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed C-Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. C-Ghost bottlenecks are designed to stack C-Ghost modules, and then the lightweight C-GhostNet can be easily established. We further consider the efficient networks for GPU devices. Without involving too many GPU-inefficient operations (e.g., depth-wise convolution) in a building stage, we propose to utilize the stage-wise feature redundancy to formulate GPU-efficient Ghost (G-Ghost) stage structure. The features in a stage are split into two parts where the first part is processed using the original block with fewer output channels for generating intrinsic features, and the other are generated using cheap operations by exploiting stage-wise redundancy. Experiments conducted on benchmarks demonstrate the effectiveness of the proposed C-Ghost module and the G-Ghost stage. C-GhostNet and G-GhostNet can achieve the optimal trade-off of accuracy and latency for CPU and GPU, respectively. MindSpore code is available at https://gitee.com/mindspore/models/pulls/1809, and PyTorch code is available at https://github.com/huawei-noah/CV-Backbones. [ABSTRACT FROM AUTHOR]

It is an important problem to design resource-efficient neural architectures. One solution is adjusting the number of channels in each layer and the number of blocks in each network stage. This paper presents a novel framework named network adjustment which considers accuracy as a function of the computational resource (e.g., FLOPs or parameters), so that architecture design becomes an optimization problem and can be solved with the gradient-based optimization method. The gradient is defined as the resource utilization ratio (RUR) of each changeable module (layer or block) in a network and is accurate only in a small neighborhood of the current status. Therefore, we estimate it using Dropout, a probabilistic operation, and optimize the network architecture iteratively. The computational overhead of the entire process is comparable to that of re-training the final model from scratch. We investigate two versions of RUR where the resource usage is measured by FLOPs and latency. Experiments on standard image classification datasets and a few base networks including ResNet and EfficientNet demonstrate the effectiveness of our approach, which consistently outperforms the pruning-based counterparts. [ABSTRACT FROM AUTHOR]

In computer vision, an important challenge to deep neural networks comes from adjusting the varying properties of different image domains. To study this problem, researchers have been investigating a practical setting in which the deep neural networks are trained on a labeled source domain and then transferred to an unlabeled or even unseen target domain. The major difficulty lies in the potential domain gap, which essentially arises from the overfitting in the source domain. Hence, it is important to introduce generalized priors to alleviate the issue. From this perspective, this paper presents a novel framework that forces visual features to align with domain-agnostic priors (DAP). Specifically, we study two kinds of priors, (i) language-guided embedding and (ii) class-level relationship, and we believe that more such priors can be constructed. Our framework, referred to as DAP, is evaluated on both unsupervised domain adaptation (UDA) and domain generalization (DG) where the target domain is unlabeled and even unseen, respectively. We use the standard benchmark that performs transfer semantic segmentation on synthesized datasets (i.e., GTAv and SYNTHIA) and a real dataset (i.e., Cityscapes). Experiments validate the effectiveness of DAP with competitive accuracy in all tasks. In particular, language-guided priors work sufficiently well for UDA, while class-level priors serve as useful complements for DG. The proposed frameworks shed light that domain transfer benefits from better proxies, possibly from other modalities. [ABSTRACT FROM AUTHOR]

With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. Like the manually designed counterparts, we desire the automatically searched architectures to have the ability of being freely transferred to different scenarios. This paper formally puts forward this problem, referred to as NAS in the wild, which explores the possibility of finding the optimal architecture in a proxy dataset and then deploying it to mostly unseen scenarios. We instantiate this setting using a currently popular algorithm named differentiable architecture search (DARTS), which often suffers unsatisfying performance while being transferred across different tasks. We argue that the accuracy drop originates from the formulation that uses a super-network for search but a sub-network for re-training. The different properties of these stages have resulted in a significant optimization gap, and consequently, the architectural parameters "over-fit" the super-network. To alleviate the gap, we present a progressive method that gradually increases the network depth during the search stage, which leads to the Progressive DARTS (P-DARTS) algorithm. With a reduced search cost (7 hours on a single GPU), P-DARTS achieves improved performance on both the proxy dataset (CIFAR10) and a few target problems (ImageNet classification, COCO detection and three ReID benchmarks). Our code is available at https://github.com/chenxin061/pdarts. [ABSTRACT FROM AUTHOR]

Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy. To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: first, strong constraints, e.g., orthogonal or similarity preserving, are used, which however are typically relaxed and lead to large accuracy drops. Second, large amounts of training batches are required to learn the up-to-date hash functions, which largely increase the learning complexity. To handle the above challenges, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed in this paper. Our key innovation lies in introducing Hadamard matrix, which is an orthogonal binary matrix built via Sylvester method. In particular, to release the need of strong constraints, we regard each column of Hadamard matrix as the target code for each class label, which by nature satisfies several desired properties of hashing codes. To accelerate the online training, LSH is first adopted to align the lengths of target code and to-be-learned binary code. We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code. Finally, extensive experiments on four widely-used benchmarks demonstrate the superior accuracy and efficiency of HMOH over various state-of-the-art methods. Codes can be available at https://github.com/lmbxmu/mycode. [ABSTRACT FROM AUTHOR]

With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually annotated bounding boxes. These UAV videos were recorded in complex real-world scenarios and pose significant new challenges, such as complex scenes, high density, small objects, and large camera motion, to the existing object detection and tracking methods. These challenges have encouraged us to define a benchmark for three fundamental computer vision tasks, namely, object detection, single object tracking (SOT) and multiple object tracking (MOT), on our UAV dataset. Specifically, our UAV benchmark facilitates evaluation and detailed analysis of state-of-the-art detection and tracking methods on the proposed UAV dataset. Furthermore, we propose a novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT. The experimental results demonstrate that our model could make tracking results more robust in both SOT and MOT, showing that the current tracking and detection methods have limitations in dealing with the proposed UAV benchmark and that further research is indeed needed. [ABSTRACT FROM AUTHOR]

State-of-the-art image classification approaches are mainly based on robust image representation, such as the bag-of-features (BoF) model or the convolutional neural network (CNN) architecture. In real applications, the orientation (left/right) of an image or an object might vary from sample to sample, whereas some handcrafted descriptors (e.g., SIFT) and network operations (e.g., convolution) are not reversal-invariant, leading to the unsatisfied stability of image features extracted from these models. To deal with, a popular solution is to augment the dataset by adding a left-right reversed copy for each image. This strategy improves the recognition accuracy to some extent, but also brings the price of almost doubled time and memory consumptions on both the training and testing stages. In this paper, we present an alternative solution based on designing reversal-invariant representation of local patterns, so that we can obtain the identical representation for an image and its left-right reversed copy. For the BoF model, we design a reversal-invariant version of SIFT descriptor named Max-SIFT, a generalized RIDE algorithm which can be applied to a large family of local descriptors. For the CNN architecture, we present a simple idea of generating reversal-invariant deep features ( RI-Deep), and, inspired by which, design reversal-invariant convolution ( RI-Conv) layers to increase the CNN capacity without increasing the model complexity. Experiments reveal consistent accuracy gain on various image classification tasks, including scene understanding, fine-grained object recognition, and large-scale visual recognition. [ABSTRACT FROM AUTHOR]

This paper considers the task of image search using the Bag-of-Words (BoW) model. In this model, the precision of visual matching plays a critical role. Conventionally, local cues of a keypoint, e.g., SIFT, are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem and enable accurate visual matching, this paper proposes to integrate discriminative cues from multiple contextual levels, i.e., local, regional, and global, via probabilistic analysis. 'True match' is defined as a pair of keypoints corresponding to the same scene location on all three levels (Fig. 1). Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches. We show that CNN feature is complementary to SIFT due to its semantic awareness and compares favorably to several other descriptors such as GIST, HSV, etc. To reduce memory usage, we propose to index CNN features outside the inverted file, communicated by memory-efficient pointers. Experiments on three benchmark datasets demonstrate that our method greatly promotes the search accuracy when CNN feature is integrated. We show that our method is efficient in terms of time cost compared with the BoW baseline, and yields competitive accuracy with the state-of-the-arts. [ABSTRACT FROM AUTHOR]