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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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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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Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyr...

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

作者： Huo, Dong Masoumzadeh, Abbas Yang, Yee-Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high;2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://***/Dong-Huo/ASPDC.

关键词： Training Degradation Deep learning computer vision Convolution Image color analysis conferences

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Human vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes

Human Vision Based 3D Point Cloud Semantic Segmentation of L...

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

作者： Yoo, Sunghwan Jeong, Yeonjeong Jameela, Maryam Sohn, Gunho York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada

ISBN: (纸本)9798350302493

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-scale input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval

Do You Remember? Dense Video Captioning with Cross-Modal Mem...

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

作者： Kim, Minkuk Kim, Hyeon Bae Moon, Jinyoung Choi, Jinwoo Kim, Seong Tae Kyung Hee Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9798350353006

There has been significant attention to the research on dense video captioning, which aims to automatically localize and caption all events within untrimmed video. Several studies introduce methods by designing dense video captioning as a multitasking problem of event localization and event captioning to consider inter-task relations. However, addressing both tasks using only visual input is challenging due to the lack of semantic content. In this study, we address this by proposing a novel framework inspired by the cognitive information processing of humans. Our model utilizes external memory to incorporate prior knowledge. The memory retrieval method is proposed with cross-modal video-to-text matching. To effectively incorporate retrieved text features, the versatile encoder and the decoder with visual and textual cross-attention modules are designed. Comparative experiments have been conducted to show the effectiveness of the proposed method on ActivityNet Captions and YouCook2 datasets. Experimental results show promising performance of our model with-out extensive pretraining from a large video dataset. Our code is available at https://***/ailab-kyunghee/CM2_DVC.

关键词： Dense Video Captioning Launguage Reasoning Retrieval Agumented Generation Video Captioning vision

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SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity recognition

SPARTAN: Self-supervised Spatiotemporal Transformers Approac...

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

作者： Chappa, Naga V. S. Raviteja Nguyen, Pha Nelson, Alexander H. Seo, Han-Seok Li, Xin Dobbs, Page Daniel Luu, Khoa University of Arkansas United States West Virginia University United States

ISBN: (纸本)9798350302493

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics1. © 2023 ieee.

关键词： computer vision

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