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The core purpose of deep metric learning is to construct an embedding space, where objects belonging to the same class are gathered together and the ones from different classes are pushed apart. Most existing approaches typically insist to inter-class characteristics, e.g., class-level information or instance-level similarity, to obtain semantic relevance of data points and get a large margin between different classes in the embedding space. However, the intra-class characteristics, e.g., local manifold structure or relative relationship within the same class, are usually overlooked in the learning process. Hence the output embeddings have limitation in retrieving a good ranking result if existing multiple positive samples. And the local data structure of embedding space cannot be fully exploited since lack of relative ranking information. As a result, the model is prone to overfitting on a train set and get low generalization on the test set (unseen classes) when losing sight of intra-class variance. This paper presents a novel self-supervised synthesis ranking auxiliary framework, which captures intra-class characteristics as well as inter-class characteristics for better metric learning. Our method designs a synthetic samples generation of polar coordinates to generate measurable intra-class variance with different strength and diversity in the latent space, which can simulate the various local structure change of intra-class in the initial data domain. And then formulates a self-supervised learning procedure to fully exploit this property and preserve it in the embedding space. As a result, the learned embedding space not only keeps inter-class discrimination but also owns subtle intra-class diversity, leading to better global and local embedding structures. Extensive experiments on five benchmarks show that our method significantly improves and outperforms the state-of-the-art methods on the performances of both retrieval and ranking by 2%-4% (personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org). [ABSTRACT FROM AUTHOR]

Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a two-step quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to fine-tune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods. [ABSTRACT FROM AUTHOR]

Unsupervised 3D model analysis has attracted tremendous attentions with the increasing growth of 3D model data and the extensive human annotations. Many effective methods have been designed to address the 3D model analysis with labeled information, while rare methods devote to unsupervised deep learning due to the difficulty of mining reliable information. In this paper, we propose a novel unsupervised deep learning method named joint local correlation and global contextual information (LCGC) for 3D model retrieval and classification, which mines the reliable triplet set and uses triplet loss to optimize the deep neural network. Our method proposes two schemes: 1) Local self-correlation information learning, which adopts the intra and inter information to construct the view-level triplet set. 2) Global neighbor contextual information learning, which employs the neighbor contextual information to explore the reliable relations among 3D models and construct the model-level triplet set. The above schemes encourage that the selected triple set can been used to improve the discrimination of learned features. Extensive evaluations on two large-scale datasets, ModelNet40 and ShapeNet55, have demonstrated the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g., drone-view cameras and satellites. It is challenging in the large visual appearance changes caused by extreme viewpoint variations. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas. In this work, we argue that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization. Specifically, we introduce a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner. Without using extra part estimators, LPN adopts a square-ring feature partition strategy, which provides the attention according to the distance to the image center. It eases the part matching and enables the part-wise representation learning. Owing to the square-ring partition design, the proposed LPN has good scalability to rotation variations and achieves competitive results on three prevailing benchmarks, i.e., University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN can be easily embedded into other frameworks to further boost performance. [ABSTRACT FROM AUTHOR]

Image captioning, a challenging task where the machine automatically describes an image with natural language, has drawn significant attention in recent years. Despite the remarkable improvements of recent approaches, however, these methods are built upon a large set of training image-sentence pairs. The expensive labor efforts hence limit the captioning model to describe the wider world. In this paper, we present a novel network structure, Cascaded Revision Network, which aims at relieving the problem by equipping the model with out-of-domain knowledge. CRN first tries its best to describe an image using the existing vocabulary from in-domain knowledge. Due to the lack of out-of-domain knowledge, the caption may be inaccurate or include ambiguous words for the image with unknown (novel) objects. We propose to re-edit the primary captioning sentence by a series of cascaded operations. We introduce a perplexity predictor to find out which words are most likely to be inaccurate given the input image. Thereafter, we utilize external knowledge from a pretrained object detection model and select more accurate words from detection results by the visual matching module. In the last step, we design a semantic matching module to ensure that the novel object is fit in the right position. By this novel cascaded captioning-revising mechanism, CRN can accurately describe images with unseen objects. We validate the proposed method with state-of-the-art performance on the held-out MSCOCO dataset as well as scale to ImageNet, demonstrating the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Frame rate up-conversion (FRUC) usually suffers from unreliable motion vectors due to the absence of the current frame to be interpolated. In addition, since the majority of video sequences are usually compressed by various coding standards to reduce the data volume, the quality of the generated frames in the FRUC will be further impaired. To address this problem, we proposed two FRUC algorithms based on deep residual network. We first present a deep residual network for the FRUC (DRNFRUC), which consists of feature extraction, feature recursive analysis, and image restoration parts with a skip connection between the input and the output of the network. The proposed DRNFRUC takes the result of an arbitrary existing FRUC method as the input and is able to significantly reduce the edge blurring and blocking artifacts when the motion of the block is violent. In addition, we proposed a deep residual network with weighted convolutional motion compensation (DRNWCMC) for the FRUC, where the convolution operations can be embedded into the motion compensation interpolation (MCI) in any existing MCI-based FRUC method. In DRNWCMC, we first devise two convolutional neural networks corresponding to the forward and backward motion compensated frames, respectively. And then, the adaptive interpolation coefficients for motion compensation are designed as two $1\times1$ convolutional kernels. Finally, the interpolation result of WCMC is fed into another convolutional neural network to further improve the performance. All the parameters involved in the DRNWCMC are trained simultaneously under the same cost function. The experimental results show that the two proposed algorithms can remarkably improve both the objective and subjective quality of the interpolated frames. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC) provides superior coding efficiency than previous video coding standards at the cost of increasing encoding complexity. The complexity increase of motion estimation (ME) procedure is rather significant, especially when considering the complicated partitioning structure of HEVC. To fully exploit the coding efficiency brought by HEVC requires a huge amount of computations. In this paper, we analyze the ME structure in HEVC and propose a parallel framework to decouple ME for different partitions on many-core processors. Based on local parallel method (LPM), we first use the directed acyclic graph (DAG)-based order to parallelize coding tree units (CTUs) and adopt improved LPM (ILPM) within each CTU (DAGILPM), which exploits the CTU-level and prediction unit (PU)-level parallelism. Then, we find that there exist completely independent PUs (CIPUs) and partially independent PUs (PIPUs). When the degree of parallelism (DP) is smaller than the maximum DP of DAGILPM, we process the CIPUs and PIPUs, which further increases the DP. The data dependencies and coding efficiency stay the same as LPM. Experiments show that on a 64-core system, compared with serial execution, our proposed scheme achieves more than 30 and 40 times speedup for $1920\times 1080$ and $2560\times 1600$ video sequences, respectively. [ABSTRACT FROM AUTHOR]