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Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation

Tell Me What Happened: Unifying Text-guided Video Completion...

引用

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

作者： Fu, Tsu-Jui Yu, Licheng Zhang, Ning Fu, Cheng-Yang Su, Jong-Chyi Wang, William Yang Bell, Sean UC Santa Barbara Santa Barbara CA 93106 USA Meta Menlo Pk CA USA NEC Labs Amer Princeton NJ USA

ISBN: (纸本)9798350301298

Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC.

关键词： and reasoning language vision

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Prototypical Residual Networks for Anomaly Detection and Localization

Prototypical Residual Networks for Anomaly Detection and Loc...

引用

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

作者： Zhang, Hui Wu, Zuxuan Wang, Zheng Chen, Zhineng Jiang, Yu-Gang Fudan Univ Shanghai Key Lab Intell Info Proc Sch CS Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Zhejiang Univ Technol Sch Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns;a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.

关键词： vision applications and systems

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Transferable Adversarial Attacks on vision Transformers with Token Gradient Regularization

Transferable Adversarial Attacks on Vision Transformers with...

引用

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

作者： Zhang, Jianping Huang, Yizhan Wu, Weibin Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based attacks, our TGR offers a performance improvement of 8.8% on average.

关键词： Adversarial attack and defense

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Robust 3D Shape Classification via Non-local Graph Attention Network

Robust 3D Shape Classification via Non-local Graph Attention...

引用

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

作者： Qin, Shengwei Li, Zhong Liu, Ligang Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China Huzhou Univ Sch Informat Huzhou Peoples R China Zhejiang Sci Tech Univ Sch Sci Hangzhou Zhejiang Peoples R China Univ Sci & Technol China Sch Math Sci Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

We introduce a non-local graph attention network (NL-GAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.

关键词： detection recognition: Categorization retrieval

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Difficulty-based Sampling for Debiased Contrastive Representation Learning

Difficulty-based Sampling for Debiased Contrastive Represent...

引用

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

作者： Jang, Taeuk Wang, Xiaoqian Purdue Univ 465 Northwestern Ave W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

Contrastive learning is a self-supervised representation learning method that achieves milestone performance in various classification tasks. However, due to its unsupervised fashion, it suffers from the false negative sample problem: randomly drawn negative samples that are assumed to have a different label but actually have the same label as the anchor. This deteriorates the performance of contrastive learning as it contradicts the motivation of contrasting semantically similar and dissimilar pairs. This raised the attention and the importance of finding legitimate negative samples, which should be addressed by distinguishing between 1) true vs. false negatives;2) easy vs. hard negatives. However, previous works were limited to the statistical approach to handle false negative and hard negative samples with hyperparameters tuning. In this paper, we go beyond the statistical approach and explore the connection between hard negative samples and data bias. We introduce a novel debiased contrastive learning method to explore hard negatives by relative difficulty referencing the bias amplifying counterpart. We propose triplet loss for training a biased encoder that focuses more on easy negative samples. We theoretically show that the triplet loss amplifies the bias in self-supervised representation learning. Finally, we empirically show the proposed method improves downstream classification performance.

关键词： accountability ethics in vision fairness privacy Transparency

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SVFormer: Semi-supervised Video Transformer for Action recognition

SVFormer: Semi-supervised Video Transformer for Action Recog...

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

作者： Xing, Zhen Dai, Qi Hu, Han Chen, Jingjing Wu, Zuxuan Jiang, Yu-Gang Fudan Univ Shanghai Key Lab Intell Info Proc Sch CS Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer models have been less explored. In this paper, we investigate the use of transformer models under the SSL setting for action recognition. To this end, we introduce SVFormer, which adopts a steady pseudo-labeling framework (i.e., EMA-Teacher) to cope with unlabeled video samples. While a wide range of data augmentations have been shown effective for semi-supervised image classification, they generally produce limited results for video recognition. We therefore introduce a novel augmentation strategy, Tube Token-Mix, tailored for video data where video clips are mixed via a mask with consistent masked tokens over the temporal axis. In addition, we propose a temporal warping augmentation to cover the complex temporal variation in videos, which stretches selected frames to various temporal durations in the clip. Extensive experiments on three datasets Kinetics-400, UCF-101, and HMDB-51 verify the advantage of SVFormer. In particular, SVFormer outperforms the state-of-the-art by 31.5% with fewer training epochs under the 1% labeling rate of Kinetics-400. Our method can hopefully serve as a strong benchmark and encourage future search on semi-supervised action recognition with Transformer networks. Code is released at https://***/ChenHsing/SVFormer.

关键词： Video: Action and event understanding

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Compression-Aware Video Super-Resolution

Compression-Aware Video Super-Resolution

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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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Making vision Transformers Efficient from A Token Sparsification View

Making Vision Transformers Efficient from A Token Sparsifica...

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

作者： Chang, Shuning Wang, Pichao Lin, Ming Wang, Fan Zhang, David Junhao Jin, Rong Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

The quadratic computational complexity to the number of tokens limits the practical applications of vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application difficulty in the local vision transformer, and (iii) non-general-purpose networks for downstream tasks. In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks. The semantic tokens represent cluster centers, and they are initialized by pooling image tokens in space and recovered by attention, which can adaptively represent global or local semantic information. Due to the cluster properties, a few semantic tokens can attain the same effect as vast image tokens, for both global and local vision transformers. For instance, only 16 semantic tokens on DeiT-(Tiny,Small,Base) can achieve the same accuracy with more than 100% inference speed improvement and nearly 60% FLOPs reduction;on Swin-(Tiny,Small,Base), we can employ 16 semantic tokens in each window to further speed it up by around 20% with slight accuracy increase. Besides great success in image classification, we also extend our method to video recognition. In addition, we design a STViT-R(ecovery) network to restore the detailed spatial information based on the STViT, making it work for downstream tasks, which is powerless for previous token sparsification methods. Experiments demonstrate that our method can achieve competitive results compared to the original networks in object detection and instance segmentation, with over 30% FLOPs reduction for backbone.

关键词： Deep learning architectures and techniques

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SOOD: Towards Semi-Supervised Oriented Object Detection

SOOD: Towards Semi-Supervised Oriented Object Detection

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

作者： Hua, Wei Liang, Dingkang Li, Jingyu Liu, Xiaolong Zou, Zhikang Ye, Xiaoqing Bai, Xiang Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://***/HamPerdredes/SOOD.

关键词： detection recognition: Categorization retrieval

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Progressive Transformation Learning for Leveraging Virtual Images in Training

Progressive Transformation Learning for Leveraging Virtual I...

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

作者： Shen, Yi-Ting Lee, Hyungtae Kwon, Heesung Bhattacharyya, Shuvra S. Univ Maryland College Pk MD USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.

关键词： detection recognition: Categorization retrieval

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