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ACTION-Net: Multipath Excitation for Action recognition

ACTION-Net: Multipath Excitation for Action Recognition

引用

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

作者： Wang, Zhengwei She, Qi Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relationships;3D CNNs can achieve good performance but are computationally intensive. In this work, we tackle this dilemma by designing a generic and effective module that can be embedded into 2D CNNs. To this end, we propose a spAtio-temporal, Channel and moTion excitatION (ACTION) module consisting of three paths: Spatio-Temporal Excitation (STE) path, Channel Excitation (CE) path, and Motion Excitation (ME) path. The STE path employs one channel 3D convolution to characterize spatio-temporal representation. The CE path adaptively recalibrates channelwise feature responses by explicitly modeling interdependencies between channels in terms of the temporal aspect. The ME path calculates feature-level temporal differences, which is then utilized to excite motion-sensitive channels. We equip 2D CNNs with the proposed ACTION module to form a simple yet effective ACTION-Net with very limited extra computational cost. ACTION-Net is demonstrated by consistently outperforming 2D CNN counterparts on three backbones (i.e., ResNet-50, MobileNet V2 and BNInception) employing three datasets (i.e., Something-Something V2, Jester, and EgoGesture). Code is provided at https://***/V-Sense/ACTION-Net.

关键词： computer vision Adaptation models Three-dimensional displays Codes Target recognition Convolution Computational modeling

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Nearest Neighbor Matching for Deep Clustering

Nearest Neighbor Matching for Deep Clustering

引用

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

作者： Dang, Zhiyuan Deng, Cheng Yang, Xu Wei, Kun Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China JD Tech Beijing 100176 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA JD Finance Amer Corp Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and global features. In addition, since the deep features are updated on-the-fly, relying on these sample relationships may construct more semantically confident sample pairs, leading to inferior performance. To tackle this issue, we propose a method called Nearest Neighbor Matching (NNM) to match samples with their nearest neighbors from both local (batch) and global (overall) levels. Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features. To keep the clustering assignment consistent in both neighbors and classes, we frame consistent loss and class contrastive loss for both local and global levels. Experimental results on three benchmark datasets demonstrate the superiority of our new model against state-of-the-art methods. Particularly on the STL-10 dataset, our method can achieve supervised performance. As for the CIFAR-100 dataset, our NNM leads 3.7% against the latest comparison method. Our code will be available at https://***/ZhiyuanDang/NNM.

关键词： computer vision Codes Semantics Artificial neural networks Benchmark testing pattern recognition Unsupervised learning

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Domain Adaptation with Auxiliary Target Domain-Oriented Classifier

Domain Adaptation with Auxiliary Target Domain-Oriented Clas...

引用

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

作者： Liang, Jian Hu, Dapeng Feng, Jiashi Natl Univ Singapore NUS Singapore Singapore Sea AI Lab SAIL Singapore Singapore

ISBN: (纸本)9781665445092

Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous methods focusing on learning domain-invariant feature representations, some recent methods present generic semi-supervised learning (SSL) techniques and directly apply them to DA tasks, even achieving competitive performance. One of the most popular SSL techniques is pseudo-labeling that assigns pseudo labels for each unlabeled data via the classifier trained by labeled data. However, it ignores the distribution shift in DA problems and is inevitably biased to source data. To address this issue, we propose a new pseudo-labeling framework called Auxiliary Target Domain-Oriented Classifier (ATDOC). ATDOC alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels. Specifically, we employ the memory mechanism and develop two types of non-parametric classifiers, i.e. the nearest centroid classifier and neighborhood aggregation, without introducing any additional network parameters. Despite its simplicity in a pseudo classification objective, ATDOC with neighborhood aggregation significantly outperforms domain alignment techniques and prior SSL techniques on a large variety of DA benchmarks and even scare-labeled SSL tasks.

关键词： computer vision Handheld computers Semantics Focusing Semisupervised learning Benchmark testing pattern recognition

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Animating General Image with Large Visual Motion Model

Animating General Image with Large Visual Motion Model

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dengsheng Chen Xiaoming Wei Xiaolin Wei Meituan Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present the pioneering Large Visual Motion Model (LVMM), meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model, fortified with a wealth of prior knowledge extracted from billions of image pairs, demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result, it can infuse any generic image with authentic dynamic effects, enhancing its visual allure. Project page: https://***/densechen/LVMM.

关键词： Visualization computer vision Analytical models Computational modeling Dynamics Predictive models pattern recognition

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Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

Frequency-aware Discriminative Feature Learning Supervised b...

引用

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

作者： Li, Jiaming Xie, Hongtao Li, Jiahong Wang, Zhongyuan Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignorable problems: a) learned features supervised by softmax loss are separable but not discriminative enough, since softmax loss does not explicitly encourage intra-class compactness and interclass separability;and b)fixed filter banks and hand-crafted features are insufficient to capture forgery patterns of frequency from diverse inputs. To compensate for such limitations, a novel frequency-aware discriminative feature learning framework is proposed in this paper. Specifically, we design a novel single-center loss (SCL) that only compresses intra-class variations of natural faces while boosting interclass differences in the embedding space. In such a case, the network can learn more discriminative features with less optimization difficulty. Besides, an adaptive frequency feature generation module is developed to mine frequency clues in a completely data-driven fashion. With the above two modules, the whole framework can learn more discriminative features in an end-to-end manner. Extensive experiments demonstrate the effectiveness and superiority of our framework on three versions of the FF++ dataset.

关键词： Measurement computer vision Face recognition Frequency-domain analysis Filter banks Boosting Forgery

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Learning a Non-blind Deblurring Network for Night Blurry Images

Learning a Non-blind Deblurring Network for Night Blurry Ima...

引用

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

作者： Chen, Liang Zhang, Jiawei Pan, Jinshan Lin, Songnan Fang, Faming Ren, Jimmy S. East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China SenseTime Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels in the deconvolution steps which are optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters for better image restoration. The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively.

关键词： computer vision Deconvolution Convolution Noise reduction Estimation Image restoration pattern recognition

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Understanding the Robustness of Skeleton-based Action recognition under Adversarial Attack

Understanding the Robustness of Skeleton-based Action Recogn...

引用

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

作者： Wang, He He, Feixiang Peng, Zhexi Shao, Tianjia Yang, Yong-Liang Zhou, Kun Hogg, David Univ Leeds Leeds W Yorkshire England Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Univ Bath Bath Avon England

ISBN: (纸本)9781665445092

Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers which rely on the 3D skeletal motion. Our method involves an innovative perceptual loss which ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements.

关键词： computer vision Three-dimensional displays Surveillance Robustness pattern recognition Autonomous vehicles

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Multi-Objective Interpolation Training for Robustness to Label Noise

Multi-Objective Interpolation Training for Robustness to Lab...

引用

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

作者： Ortego, Diego Arazo, Eric Albert, Paul O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9781665445092

Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robust classification loss functions. Conversely, we propose a Multi-Objective Interpolation Training (MOIT) approach that jointly exploits contrastive learning and classification to mutually help each other and boost performance against label noise. We show that standard supervised contrastive learning degrades in the presence of label noise and propose an interpolation training strategy to mitigate this behavior. We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples. This detection allows treating noisy samples as unlabeled and training a classifier in a semi-supervised manner to prevent noise memorization and improve representation learning. We further propose MOIT+, a refinement of MOLT by fine-tuning on detected clean samples. Hyperparameter and ablation studies verify the key components of our method. Experiments on synthetic and real-world noise benchmarks demonstrate that MOIT/MOIT+ achieves state-of-the-art results.

关键词： Training Deep learning Interpolation Prototypes Robustness Noise robustness pattern recognition

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Background Splitting: Finding Rare Classes in a Sea of Background

Background Splitting: Finding Rare Classes in a Sea of Backg...

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

作者： Mullapudi, Ravi Teja Poms, Fait Mark, William R. Ramanan, Deva Fatahalian, Kayvon Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dataset. We find that state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in this regime. Our solution is to split the large, visually diverse background into many smaller, visually similar categories during training. We implement this idea by extending an image classification model with an additional auxiliary loss that learns to mimic the predictions of a pre-existing classification model on the training set. The auxiliary loss requires no additional human labels and regularizes feature learning in the shared network trunk by forcing the model to discriminate between auxiliary categories for all training set examples, including those belonging to the monolithic background of the main rare category classification task. To evaluate our method we contribute modified versions of the iNaturalist and Places365 datasets where only a small subset of rare category labels are available during training (all other images are labeled as background). By jointly learning to recognize both the selected rare categories and auxiliary categories, our approach yields models that perform 8.3 mAP points higher than stateof-the-art imbalanced learning baselines when 98.30% of the data is background, and up to 42.3 mAP points higher than fine-tuning baselines when 99.98% of the data is background.

关键词： Training computer vision Computational modeling Predictive models Data models pattern recognition Task analysis

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RSG: A Simple but Effective Module for Learning Imbalanced Datasets

RSG: A Simple but Effective Module for Learning Imbalanced D...

引用

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

作者： Wang, Jianfeng Lukasiewicz, Thomas Hu, Xiaolin Cai, Jianfei Xu, Zhenghua Univ Oxford Oxford England Tsinghua Univ Beijing Peoples R China Monash Univ Melbourne Vic Australia Hebei Univ Technol Tianjin Peoples R China

ISBN: (纸本)9781665445092

Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes during training, and it has in particular the following advantages: (1) it is convenient to use and highly versatile, because it can be easily integrated into any kind of convolutional neural network, and it works well when combined with different loss functions, and (2) it is only used during the training phase, and therefore, no additional burden is imposed on deep neural networks during the testing phase. In extensive experimental evaluations, we verify the effectiveness of RSG. Furthermore, by leveraging RSG, we obtain competitive results on Imbalanced CIFAR and new state-of-the-art results on Places-LT, ImageNet-LT, and iNaturalist 2018. The source code is available at https://***-Wang/RSG.

关键词： Training Deep learning computer vision Codes Generators pattern recognition Convolutional neural networks

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