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CDAD: A Common Daily Action Dataset with Collected Hard Negative Samples

CDAD: A Common Daily Action Dataset with Collected Hard Nega...

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

作者： Xiang, Wangmeng Li, Chao Li, Ke Wang, Biao Hua, Xian-Sheng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The research on action understanding has achieved significant progress with the establishment of various benchmark datasets. However, the results of action understanding are far from satisfactory in practice. One reason is that the existing action datasets ignore the existence of many hard negative samples in real-world scenarios, which are usually undefined confusion actions, e.g., holding a pen near the mouth vs. smoking. In this work, we focus on the common actions in our daily life and present a novel Common Daily Action Dataset (CDAD), which consists of 57,824 video clips of 23 well-defined common daily actions with rich manual annotations. Particularly, for each daily action, we collect not only diverse positive samples but also various hard negative samples that have minor differences (share similarities) in action with the positive ones. The established CDAD dataset could not only serve as a benchmark for several important daily action understanding tasks, including multi-label action recognition, temporal action localization, and spatial-temporal action detection, but also provide a testbed for researchers to investigate the influence of highly similar negative samples in learning action understanding models.

关键词： Location awareness computer vision Codes conferences Computational modeling Mouth Manuals

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Doppelganger Saliency: Towards More Ethical Person Re-Identification

Doppelganger Saliency: Towards More Ethical Person Re-Identi...

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

作者： RichardWebster, Brandon Hu, Brian Fieldhouse, Keith Hoogs, Anthony Kitware Inc 1712 Route 9Suite 300 Clifton Pk NY 12065 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Modern surveillance systems have become increasingly dependent on artificial intelligence to provide actionable information for real-time decision making. A critical question relates to how these systems handle difficult ethical dilemmas, such as the re-identification of similar looking individuals. Potential misidentification of individuals can have severe negative consequences, as evidenced by recent headlines of individuals who were wrongly targeted for crimes they did not commit based on false matches. A computer vision-based saliency algorithm is proposed to help identify pixel-level differences in pairs of images containing visually similar individuals, which we term "doppelgangers." The computed saliency maps can alert human users of the presence of doppelg angers and provide important visual evidence to reduce the potential of false matches in these high-stakes situations. We show both qualitative and quantitative saliency results on doppelg angers found in a video-based person re-identification dataset (MARS) using three different state-of-the-art models. Our results suggest that this novel use of visual saliency can improve overall outcomes by helping human users in the person re-identification setting, while assuring the ethical and trusted operation of surveillance systems.

关键词： Ethics Visualization computer vision Surveillance conferences Computational modeling Decision making

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Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression recognition

Coarse-to-Fine Cascaded Networks with Smooth Predicting for ...

引用

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

作者： Xue, Fanglei Tan, Zichang Zhu, Yu Ma, Zhongsong Guo, Guodong Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://***/BR-IDL/PaddleViT.

关键词： Human computer interaction computer vision Codes Face recognition conferences Behavioral sciences

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Multi-modal Aerial View Object Classification Challenge Results - PBVS 2022

Multi-modal Aerial View Object Classification Challenge Resu...

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

作者： Low, Spencer Nina, Oliver Sappa, Angel D. Blasch, Erik Brigham Young Univ Provo UT 84602 USA Air Force Res Lab Dayton OH USA ESPOL Polytech Univ Ecuador Comp Vision Ctr Guayaquil Ecuador Air Force Off Sci Res Arlington VA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied COincident Optical and Radar for recognition (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.

关键词： Training Laser radar Image recognition Radar imaging Optical imaging pattern recognition Task analysis

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What Should Be Equivariant In Self-Supervised Learning

What Should Be Equivariant In Self-Supervised Learning

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

作者： Xie, Yuyang Wen, Jianhong Lau, Kin Wai Rehman, Yasar Abbas Ur Shen, Jiajun TCL AI Lab Hong Kong Peoples R China Fuzhou Univ Fuzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Self-supervised learning (SSL) aims to learn feature representation without human-annotated data. Existing methods approach this goal by encouraging the feature representations to be invariant under a set of task-irrelevant transformations and distortions defined a priori. However, multiple studies have shown that such an assumption often limits the expressive power of the representations and model would perform poorly when downstream tasks violate this assumption. For example, being invariant to rotations would prevent features from retaining enough information to estimate object rotation angles. This suggests additional manual work and domain knowledge are required for selecting augmentation types during SSL. In this work, we relax the transformation-invariance assumption by introducing a SSL framework that encourages the feature representations to preserve the order of transformation scale in embedding space for some transformations while maintaining invariance to other transformations. This allows the learned feature representations to retain information about task-relevant transformations. In addition, this framework gives rise to a handy mechanism to determine the augmentation types to which the features representations should be invariant and equivariant during SSL. We demonstrate the effectiveness of our method on various datasets such as Fruits 360, Caltech-UCSD Birds 200, and Blood cells dataset.

关键词： computer vision conferences Self-supervised learning Cells (biology) Feature extraction Distortion Birds

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Information Elevation Network for Online Action Detection and Anticipation

Information Elevation Network for Online Action Detection an...

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

作者： Min, Sunah Moon, Jinyoung Elect & Telecommun Res Inst ETRI Daejeon South Korea Univ Sci & Technol UST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Given a partially observed video segment, online action detection and anticipation aim to identify a current action and forecast future actions, respectively. To detect actions in a streaming video for monitoring applications including surveillance, robot assistants, and autonomous driving, online action detection methods have been proposed. Considering the importance of current action in online action detection, we introduce a novel information elevation unit (IEU) that lifts and accumulates the past information relevant to the current action, to compensate for forgotten essential information. Using the IEUs, we propose an information elevation network (IEN) that effectively identifies a current action and anticipates future actions through the dense prediction of past and current action classes within the video segment. For its practical use in online monitoring applications, our IEN takes visual features extracted from a fast action recognition using only RGB frames because extracting optical flows requires heavy computation overhead. On THUMOS-14 and TVSeries, our IEN outperforms state-of-the-art methods using only RGB frames. Furthermore, on the THUMOS-14 dataset, our IEN outperforms the state-of-the-art methods.

关键词： Visualization computer vision Surveillance conferences Color Streaming media Logic gates

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DeSI: Deepfake Source Identifier for Social Media

DeSI: Deepfake Source Identifier for Social Media

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

作者： Narayan, Kartik Agarwal, Harsh Mittal, Surbhi Thakral, Kartik Kundu, Suman Vatsa, Mayank Singh, Richa IIT Jodhpur Karwar Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Social media holds the power to influence a significant change in the population. Through social media, people all around the world can connect and share their views. However, this social space is now infected due to the infiltration of fraudulent, obscene, fake and possibly, influential media. According to a UNESCO report, prevalence of fake news and deepfake content possess the potential of spreading fake propaganda and can lead to political and social unrest. Trust on social media is an emerging problem and there is an urgent need to address the same. There has been some research around approaches that detect fake news and deepfakes, however, identification of the source of these deepfakes posted on social media platforms is an equally important but relatively unexplored challenge. This paper proposes a novel Deepfake Source Identification (DeSI) algorithm that identifies the sources of deepfakes posted on Twitter. The proposed DeSI algorithm allows for two input modalities - text and images. We rigorously test our algorithm in both constrained and unconstrained experimental setups and report the observed results. In the constrained setting, the algorithm correctly identifies all the deepfake tweets as well their sources. The complete framework is further encased in a web portal to facilitate intuitive use and analysis of the results.

关键词： Deepfakes computer vision Social networking (online) Scalability conferences Blogs Sociology

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ConvMLP: Hierarchical Convolutional MLPs for vision

ConvMLP: Hierarchical Convolutional MLPs for Vision

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Li, Jiachen Hassani, Ali Walton, Steven Shi, Humphrey SHI Lab University of Oregon UIUC United States

ISBN: (纸本)9798350302493

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods on image classification. However, most methods adopt spatial MLPs which take fixed-dimension inputs, therefore making it difficult to apply them as backbones to downstream tasks such as object detection and semantic segmentation, which require inputs with arbitrary dimension. Moreover, single-stage designs further limit the performance in other computer vision tasks and fully-connected layers bear heavy computation. To tackle these problems, we propose ConvMLP: a Hierarchical Convolutional MLP for visual recognition, which is a lightweight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4 GMACs (15% and 19% of MLP-Mixer-B/16, respectively). Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred to downstream tasks and achieve competitive results with fewer parameters. Our code and pre-trained models are open-sourced at https://***/SHI-Labs/Convolutional-MLPs. © 2023 ieee.

关键词： Object detection

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Posture-based Infant Action recognition in the Wild with Very Limited Data

Posture-based Infant Action Recognition in the Wild with Ver...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Huang, Xiaofei Luan, Lingfei Hatamimajoumerd, Elaheh Wan, Michael Kakhaki, Pooria Daneshvar Obeid, Rita Ostadabbas, Sarah Northeastern University MA United States Case Western Reserve University OH United States

ISBN: (纸本)9798350302493

Automatic detection of infant actions from home videos could aid medical and behavioral specialists in the early detection of motor impairments in infancy. However, most computer vision approaches for action recognition are centered around adult subjects, following datasets and benchmarks in the field. In this work, we present a data-efficient pipeline for infant action recognition based on the idea of modeling an action as a time sequence consisting of two different stable postures with a transition period between them. The postures are detected frame-wise from the estimated 2D and 3D infant body poses and the action sequence is segmented based on the posture-driven low-dimensional features of each frame. To spur further research in the field, we also created and release the first-of-its-kind infant action dataset - InfAct - consisting of 200 fully annotated home videos representing a wide range of common infant actions, intended as a public benchmark. Among the ten more common classes of infant actions, our action recognition model achieved 78.0% accuracy when tested on InfAct, highlighting the promise of video-based infant action recognition as a viable monitoring tool for infant motor development1. © 2023 ieee.

关键词： computer vision

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CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

CENet: Consolidation-and-Exploration Network for Continuous ...

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

作者： Zhang, Chi Cheng, Yalu Wei, Pengxu He, Hongliang Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework-Consolidationand-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods.

关键词： Representation learning Adaptation models computer vision Computational modeling conferences Reliability engineering Data models

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