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Efficient deformable shape correspondence via multiscale spectral manifold wavelets preservation

Efficient deformable shape correspondence via multiscale spe...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Ling Li, Qinsong Liu, Shengjun Liu, Xinru Cent South Univ Inst Engn Modeling & Sci Comp Changsha Peoples R China Hunan First Normal Univ Sch Math & Computat Sci Changsha Peoples R China Cent South Univ State Key Lab High Performance Mfg Complex Changsha Peoples R China

ISBN: (纸本)9781665445092

The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this framework is to formulate suitable preservation constraints to encode the geometric information that must be preserved by the unknown map. For this issue, we construct novel and powerful constraints to determine the functional map, where multiscale spectral manifold wavelets are required to be preserved at each scale correspondingly. Such constraints allow us to extract significantly more information than previous methods, especially those based on descriptor preservation constraints, and strongly ensure the isometric property of the map. In addition, we also propose a remarkable efficient iterative method to alternatively update the functional maps and pointwise maps. Moreover, when we use the tight wavelet frames in iterations, the computation of the functional maps boils down to a simple filtering procedure with low-pass and various band-pass filters, which avoids time-consuming solving large systems of linear equations commonly presented in functional maps. We demonstrate on a wide variety of experiments with different datasets that our approach achieves significant improvements both in the shape correspondence quality and the computing efficiency.

关键词： Manifolds Band-pass filters computer vision Shape Filtering Color Iterative methods

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Generative Zero-shot Network Quantization

Generative Zero-shot Network Quantization

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Xiangyu Lu, Jiahao Xu, Weixiang Hu, Qinghao Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665448994

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, e.g., due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.

关键词： Training Quantization (signal) Image resolution Image synthesis Training data pattern recognition Image restoration

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ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning

ECKPN: Explicit Class Knowledge Propagation Network for Tran...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Chaofan Yang, Xiaoshan Xu, Changsheng Huang, Xuhui Ma, Zhe Univ Sci & Technol China USTC Sch Informat Sci & Technol Hefei Anhui Peoples R China Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Second Acad CASIC X Lab Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the art methods.

关键词： Visualization computer vision Art Computational modeling Knowledge representation Benchmark testing Calibration

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Learning Salient Boundary Feature for Anchor-free Temporal Action Localization

Learning Salient Boundary Feature for Anchor-free Temporal A...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lin, Chuming Xu, Chengming Luo, Donghao Wang, Yabiao Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Fu, Yanwei Tencent Youtu Lab Shenzhen Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Temporal action localization is an important yet challenging task in video understanding. Typically, such a task aims at inferring both the action category and localization of the start and end frame for each action instance in a long, untrimmed video. While most current models achieve good results by using pre-defined anchors and numerous actionness, such methods could be bothered with both large number of outputs and heavy tuning of locations and sizes corresponding to different anchors. Instead, anchor-free methods is lighter, getting rid of redundant hyper-parameters, but gains few attention. In this paper, we propose the first purely anchor-free temporal localization method, which is both efficient and effective. Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module to gather more valuable boundary features for each proposal with a novel boundary pooling, and (iii) several consistency constraints to make sure our model can find the accurate boundary given arbitrary proposals. Extensive experiments show that our method beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3. Code is available at https://***/TencentYoutuResearch/ActionDetection - AFSD.

关键词： Location awareness computer vision Design methodology Computational modeling Predictive models Feature extraction pattern recognition

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Bi-GCN: Binary Graph Convolutional Network

Bi-GCN: Binary Graph Convolutional Network

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Junfu Wang, Yunhong Yang, Zhen Yang, Liang Guo, Yuanfang Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781665445092

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of similar to 30x for both the network parameters and input data, and accelerate the inference speed by an average of similar to 47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments.

关键词： computer vision Memory management Loading Graph neural networks pattern recognition Task analysis

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Subjective Quality Optimized Efficient Image Compression

Subjective Quality Optimized Efficient Image Compression

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xining Chen, Tong Ma, Zhan Nanjing Univ Vis Lab Nanjing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose an efficient image compression framework that is optimized for subjective quality. Our framework is mainly based on the NLAIC (NonLocal Attention Optimized Image Coding) model which applied Variational Autoencoder (VAE) and non-local attention module to end-to-end image compression. This work makes two major contributions to the NLAIC framework. First, our models are optimized for subjective-friendly loss functions rather than conventional MSE (Mean Squared Error) or MS-SSIM (Multiscale Structural Similarity) which was widely used in previous works. Second, we introduce block-based inference mechanism to reduce the running memory consumption of the image compression network, and suggest a partial post-processing step to alleviate block artifacts caused by block-based inference in a lightweight computational fashion. Experiments have proved that the image reconstructed by our method can preserve more texture details than models trained for optimal MSE or MS-SSIM and also present capability for high-throughput decoding.

关键词： Measurement computer vision Image coding Inference mechanisms conferences Memory management Distortion

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Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

Implicit Feature Alignment: Learn to Convert Text Recognizer...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Tianwei Zhu, Yuanzhi Jin, Lianwen Peng, Dezhi Li, Zhe He, Mengchao Wang, Yongpan Luo, Canjie South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Guangdong Artificial Intelligence & Digital Econ Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still constrained to solve the problem of reading cropped line text images and serves as a subtask of optical character recognition (OCR) systems. As a result, the final text recognition result is limited by the performance of the text detector. In this paper, we propose a simple, elegant and effective paradigm called Implicit Feature Alignment (IFA), which can be easily integrated into current text recognizers, resulting in a novel inference mechanism called IFA-inference. This enables an ordinary text recognizer to process multi-line text such that text detection can be completely freed. Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore, the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to suppress negative predictions to assist in training ADP and ExCTC. We experimentally demonstrate that IFA achieves state-of-the-art performance on end-to-end document recognition tasks while maintaining the fastest speed, and ADP and ExCTC complement each other on the perspective of different application scenarios. Code will be available at https://***/Wang-Tianwei/Implicit-feature-alignment.

关键词： Training Text recognition Inference mechanisms Pipelines Neural networks Optical detectors Optical imaging

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Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding

Deep Learning based Spatial-Temporal In-loop filtering for V...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665448994

The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.

关键词： Video coding Optical filters Filtering Delays Decoding pattern recognition Convolutional neural networks

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Probabilistic Model Distillation for Semantic Correspondence

Probabilistic Model Distillation for Semantic Correspondence

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Xin Fan, Deng-Ping Yang, Fan Luo, Ao Cheng, Hong Liu, Zicheng Grp 42 G42 Abu Dhabi U Arab Emirates Inception Inst AI Abu Dhabi U Arab Emirates Megvii Technol Beijing Peoples R China UESTC Chengdu Peoples R China Microsoft Redmond WA USA

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Semantic correspondence is a fundamental problem in computer vision, which aims at establishing dense correspondences across images depicting different instances under the same category. This task is challenging due to large intra-class variations and a severe lack of ground truth. A popular solution is to learn correspondences from synthetic data. However, because of the limited intra-class appearance and background variations within synthetically generated training data, the model's capability for handling "real" image pairs using such strategy is intrinsically constrained. We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs. A probabilistic supervision reweighting (PSR) module together with a confidence-aware loss (CAL) is used to mine the useful knowledge and alleviate the impact of errors. Experimental results on a variety of benchmarks show that our PMD achieves state-of-the-art performance. To demonstrate the generalizability of our approach, we extend PMD to incorporate stronger supervision for better accuracy - the probabilistic teacher is trained with stronger key-point supervision. Again, we observe the superiority of our PMD. The extensive experiments verify that PMD is able to infer more reliable supervision signals from the probabilistic teacher for representation learning and largely alleviate the influence of errors in pseudo labels. Cade is avaliable at https://***/fanyang587/PMD.

关键词： computer vision Codes Annotations Semantics Training data Probabilistic logic Data models

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ReDet: A Rotation-equivariant Detector for Aerial Object Detection

ReDet: A Rotation-equivariant Detector for Aerial Object Det...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Han, Jiaming Ding, Jian Xue, Nan Xia, Gui-Song Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781665445092

Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model the orientation variation, large amounts of rotation augmented data is needed to train an accurate object detector. In this paper, we propose a Rotation-equivariant Detector (ReDet) to address these issues, which explicitly encodes rotation equivariance and rotation invariance. More precisely, we incorporate rotation-equivariant networks into the detector to extract rotation-equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size. Based on the rotation-equivariant features, we also present Rotation-invariant RoI Align (RiRoI Align), which adaptively extracts rotation-invariant features from equivariant features according to the orientation of RoI. Extensive experiments on several challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that our method can achieve state-of-the-art performance on the task of aerial object detection. Compared with previous best results, our ReDet gains 1.2, 3.5 and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing the number of parameters by 60% (313 Mb vs. 121 Mb).

关键词： computer vision Codes Computational modeling Detectors Object detection Predictive models Feature extraction

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