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Compression-Aware Video Super-Resolution

Compression-Aware Video Super-Resolution

引用

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

作者： Wang, Yingwei Isobe, Takashi Jia, Xu Tao, Xin Lu, Huchuan Tai, Yu-Wing Dalian Univ Technol Dalian Peoples R China Xiaohongshu Inc Shanghai Peoples R China Kuaishou Technol Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications. In spite of a few pioneering works being proposed recently to super-resolve the compressed videos, they are not specially designed to deal with videos of various levels of compression. In this paper, we propose a novel and practical compression-aware video super-resolution model, which could adapt its video enhancement process to the estimated compression level. A compression encoder is designed to model compression levels of input frames, and a base VSR model is then conditioned on the implicitly computed representation by inserting compression-aware modules. In addition, we propose to further strengthen the VSR model by taking full advantage of meta data that is embedded naturally in compressed video streams in the procedure of information fusion. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method on compressed VSR benchmarks. The codes will be available at https://***/aprBlue/CAVSR

关键词： Low-level vision

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SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution vision Transformer

SparseViT: Revisiting Activation Sparsity for Efficient High...

引用

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

作者： Chen, Xuanyao Liu, Zhijian Tang, Haotian Yi, Li Zhao, Hang Han, Song Shanghai Qi Zhi Inst Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China MIT Cambridge MA USA

ISBN: (纸本)9798350301298

High-resolution images enable neural networks to learn richer visual representations. However, this improved performance comes at the cost of growing computational complexity, hindering their usage in latency-sensitive applications. As not all pixels are equal, skipping computations for less-important regions offers a simple and effective measure to reduce the computation. This, however, is hard to be translated into actual speedup for CNNs since it breaks the regularity of the dense convolution workload. In this paper, we introduce SparseViT that revisits activation sparsity for recent window-based vision transformers (ViTs). As window attentions are naturally batched over blocks, actual speedup with window activation pruning becomes possible: i.e., similar to 50% latency reduction with 60% sparsity. Different layers should be assigned with different pruning ratios due to their diverse sensitivities and computational costs. We introduce sparsity-aware adaptation and apply the evolutionary search to efficiently find the optimal layerwise sparsity configuration within the vast search space. SparseViT achieves speedups of 1.5x, 1.4x, and 1.3x compared to its dense counterpart in monocular 3D object detection, 2D instance segmentation, and 2D semantic segmentation, respectively, with negligible to no loss of accuracy.

关键词： Efficient and scalable vision

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OvarNet: Towards Open-vocabulary Object Attribute recognition

OvarNet: Towards Open-vocabulary Object Attribute Recognitio...

引用

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

作者： Chen, Keyan Jiang, Xiaolong Hu, Yao Tang, Xu Gao, Yan Chen, Jianqi Xie, Weidi Beihang Univ Beijing Peoples R China Xiaohongshu Inc Shanghai Peoples R China Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario. To achieve this goal, we make the following contributions: (i) we start with a naive two-stage approach for open-vocabulary object detection and attribute classification, termed CLIP-Attr. The candidate objects are first proposed with an offline RPN and later classified for semantic category and attributes;(ii) we combine all available datasets and train with a federated strategy to fine-tune the CLIP model, aligning the visual representation with attributes, additionally, we investigate the efficacy of leveraging freely available online image-caption pairs under weakly supervised learning;(iii) in pursuit of efficiency, we train a Faster-RCNN type model end-to-end with knowledge distillation, that performs class-agnostic object proposals and classification on semantic categories and attributes with classifiers generated from a text encoder;Finally, (iv) we conduct extensive experiments on VAW, MSCOCO, LSA, and OVAD datasets, and show that recognition of semantic category and attributes is complementary for visual scene understanding, i.e., jointly training object detection and attributes prediction largely outperform existing approaches that treat the two tasks independently, demonstrating strong generalization ability to novel attributes and categories.

关键词： detection recognition: Categorization retrieval

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PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training

PEFAT: Boosting Semi-supervised Medical Image Classification...

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

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China Univ Adelaide Adelaide SA 5005 Australia

ISBN: (纸本)9798350301298

Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multiclass and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://***/maxwell0027/PEFAT.

关键词： cell microscopy Medical and biological vision

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Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed recognition

Class-Conditional Sharpness-Aware Minimization for Deep Long...

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

作者： Zhou, Zhipeng Li, Lanqing Zhao, Peilin Heng, Pheng-Ann Gong, Wei Univ Sci & Technol China Hefei Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9798350301298

It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better. However, such property is under-explored in deep long-tailed recognition (DLTR), a practical problem where the model is required to generalize equally well across all classes when trained on highly imbalanced label distribution. In this paper, through empirical observations, we argue that sharp minima are in fact prevalent in deep longtailed models, whereas naive integration of existing flattening operations into long-tailed learning algorithms brings little improvement. Instead, we propose an effective two-stage sharpness-aware optimization approach based on the decoupling paradigm in DLTR. In the first stage, both the feature extractor and classifier are trained under parameter perturbations at a class-conditioned scale, which is theoretically motivated by the characteristic radius of flat minima under the PAC-Bayesian framework. In the second stage, we generate adversarial features with class-balanced sampling to further robustify the classifier with the backbone frozen. Extensive experiments on multiple longtailed visual recognition benchmarks show that, our proposed Class-Conditional Sharpness-Aware Minimization (CC-SAM), achieves competitive performance compared to the state-of-the-arts. Code is available at https://***/zzpustc/CC-SAM.

关键词： continual low-shot meta or long-tail learning Transfer

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Enlarging Instance-specific and Class-specific Information for Open-set Action recognition

Enlarging Instance-specific and Class-specific Information f...

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

作者： Cen, Jun Zhang, Shiwei Wang, Xiang Pei, Yixuan Qing, Zhiwu Zhang, Yingya Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Hangzhou Peoples R China Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (纸本)9798350301298

Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertainty scores but dismiss the importance of feature representations. We find that features with richer semantic diversity can significantly improve the open-set performance under the same uncertainty scores. In this paper, we begin with analyzing the feature representation behavior in the open-set action recognition (OSAR) problem based on the information bottleneck (IB) theory, and propose to enlarge the instance-specific (IS) and class-specific (CS) information contained in the feature for better performance. To this end, a novel Prototypical Similarity Learning (PSL) framework is proposed to keep the instance variance within the same class to retain more IS information. Besides, we notice that unknown samples sharing similar appearances to known samples are easily misclassified as known classes. To alleviate this issue, video shuffling is further introduced in our PSL to learn distinct temporal information between original and shuffled samples, which we find enlarges the CS information. Extensive experiments demonstrate that the proposed PSL can significantly boost both the open-set and closed-set performance and achieves state-of-the-art results on multiple benchmarks. Code is available at https://***/Jun-CEN/PSL.

关键词： Video: Action and event understanding

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Pointersect: Neural Rendering with Cloud-Ray Intersection

Pointersect: Neural Rendering with Cloud-Ray Intersection

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

作者： Chang, Jen-Hao Rick Chen, Wei -Yu Raujan, Anurag Yi, Kwang Moo Tuzel, Oncel Apple Cupertino CA 95014 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9798350301298

We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface normal estimation, rendering room-scale point clouds, inverse rendering, and ray tracing with global illumination. Unlike existing work that focuses on converting point clouds to other representations-e.g., surfaces or implicit functions-our key idea is to directly infer the intersection of a light ray with the underlying surface represented by the given point cloud. Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray. Localizing the problem into small neighborhoods enables us to train a model with only 48 meshes and apply it to unseen point clouds. Our model achieves higher estimation accuracy than state-of-the-art surface reconstruction and point-cloud rendering methods on three test sets. When applied to room-scale point clouds, without any scene-specific optimization, the model achieves competitive quality with the state-of-the-art novel-view rendering methods. Moreover, we demonstrate ability to render and manipulate Lidar-scanned point clouds such as lighting control and object insertion.

关键词： vision + graphics

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Recognizability Embedding Enhancement for Very Low-Resolution Face recognition and Quality Estimation

Recognizability Embedding Enhancement for Very Low-Resolutio...

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

作者： Chai, Jacky Chen Long Ng, Tiong-Sik Low, Cheng-Yaw Park, Jaewoo Teoh, Andrew Beng Jin Yonsei Univ Seoul South Korea Inst for Basic Sci Korea Daejeon South Korea

ISBN: (纸本)9798350301298

Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility attention mechanism is introduced to attend to the most recognizable face regions, which offers better explanatory and discriminative traits for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, our extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods.

关键词： Biometrics

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OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

OSRT: Omnidirectional Image Super-Resolution with Distortion...

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

作者： Yu, Fanghua Wang, Xintao Cao, Mingdeng Li, Gen Shan, Ying Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG ARC Shenzhen Peoples R China Univ Tokyo Tokyo Japan Tencent Online Video Platform Technol Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

关键词： Low-level vision

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Rethinking the Learning Paradigm for Dynamic Facial Expression recognition

Rethinking the Learning Paradigm for Dynamic Facial Expressi...

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

作者： Wang, Hanyang Li, Bo Wu, Shuang Shen, Siyuan Liu, Feng Ding, Shouhong Zhou, Aimin East China Normal Univ Shanghai Inst AI Educ Shanghai Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Tencent Youtu Lab Beijing Peoples R China East China Normal Univ Shanghai Int Sch Chief Technol Officer Shanghai Peoples R China

ISBN: (纸本)9798350301298

Dynamic Facial Expression recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format. Previous research has considered non-target frames as noisy frames, but we propose that it should be treated as a weakly supervised problem. We also identify the imbalance of short- and long-term temporal relationships in DFER. Therefore, we introduce the Multi-3D Dynamic Facial Expression Learning (M3DFEL) framework, which utilizes Multi-Instance Learning (MIL) to handle inexact labels. M3DFEL generates 3D-instances to model the strong short-term temporal relationship and utilizes 3DCNNs for feature extraction. The Dynamic Long-term Instance Aggregation Module (DLIAM) is then utilized to learn the long-term temporal relationships and dynamically aggregate the instances. Our experiments on DFEW and FERV39K datasets show that M3DFEL outperforms existing state-of-the-art approaches with a vanilla R3D18 backbone. The source code is available at https://***/faceeyes/M3DFEL.

关键词： body gesture Humans: Face movement pose

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