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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是131-140 订阅

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Learning Transferable Compound Expressions from Masked AutoEncoder Pretraining

Learning Transferable Compound Expressions from Masked AutoE...

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

作者： Qiu, Feng Du, Wining Zhang, Wei Liu, Chen Li, Lincheng Guo, Tianchen Yu, Xin Netease Fuxi AI Lab Guangzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9798350365474

Video-based Compound Expression recognition (CER) aims to identify compound expressions in everyday interactions per frame. Unlike rapid progress in Facial Expression recognition (FER) for the basic emotions (e.g., surprised, sad, and fearful), CER with the compound emotions (e.g., fearfully surprised, and sadly fearful) remains underexplored, with an evident gap in the availability of substantial datasets. In this paper, we design a framework to demonstrate the feasibility of predicting compound expressions in-the-wild without relying on domain-specific supervision. To be specific, we first train a model on a large-scale facial dataset using the Masked Autoencoder (MAE) approach to learn comprehensive facial features. Then, to tailor it for facial expression analysis, we fine-tune the ViT encoder on an Action Unit (AU) detection task. To address the issue of insufficient data, we transform the task of recognizing compound emotions into a multi-label recognition task for basic emotions. We train a network by fine-tuning the pretrained ViT encoder to predict the probability of each basic emotion, and then combine these probabilities to arrive at the final prediction for the compound emotions. Experiments conducted on the C-EXPR-DB dataset demonstrate the effectiveness of our framework in the frame-by-frame prediction of compound expressions in-the-wild. Our framework is recognized as the leading solution in the Compound Expression (CE) recognition Challenge in the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： affective behavior analysis compound expression recognition

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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

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

作者： Lu, Hao Niu, Xuesong Wang, Jiyao Wang, Yin Hu, Qingyong Tang, Jiaqi Zhang, Yuting Yuan, Kaishen Huang, Bin Yu, Zitong He, Dengbo Deng, Shuiguang Chen, Hao Chen, Yingcong Shan, Shiguang Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Zhejiang Univ Beijing Inst Gen Artificial Intelligence Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Great Bay Univ Portsmouth NH USA Beihang Univ Hangzhou Res Inst Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350365474

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with taskrelated agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Emotion recognition

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

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

作者： Shoman, Maged Wang, Dongdong Aboah, Armstrong Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL 32816 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA Univ Cent Florida Joint Appointment Dept Comp Sci Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL USA

ISBN: (纸本)9798350365474

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The opensource code will be available at https://***/UCF-SST-Lab/AICity2024cvprW

关键词： computer vision cross-modality learning dense video captioning domain adaptation natural language processing

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Event-Based Eye Tracking. AIS 2024 Challenge Survey

Event-Based Eye Tracking. AIS 2024 Challenge Survey

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

作者： Wang, Zuowen Gao, Chang Wu, Zongwei Conde, Marcos, V Timofte, Radu Liu, Shih-Chii Chen, Qinyu Zha, Zheng-jun Zhai, Wei Han, Han Liao, Bohao Wu, Yuliang Wan, Zengyu Wang, Zhong Cao, Yang Tan, Ganchao Chen, Jinze Pei, Yan Ru Bruers, Sasskia Crouzet, Sebastien McLelland, Douglas Coenen, Oliver Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Bich, Philippe Boretti, Chiara Prono, Luciano Lica, Mircea Dinucu-Jianu, David Griu, Catalin Lin, Xiaopeng Ren, Hongwei Cheng, Bojun Zhang, Xinan Vial, Valentin Yezzi, Anthony Tsai, James Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Delft Univ Technol Delft Netherlands Univ Wurzburg Wurzburg Germany Leiden Univ Leiden Netherlands Univ Sci & Technol China Hefei Anhui Peoples R China Brainchip Inc Laguna Hills CA USA Univ Hong Kong Hong Kong Peoples R China Politecn Torino Turin Italy Hong Kong Univ Sci & Technol Guangzhou Guangzhou Guangdong Peoples R China Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350365474

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.

关键词： computer vision dynamic vision sensor event camera eye tracking

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Vim4Path: Self-Supervised vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

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

作者： Nasiri-Sarvi, Ali Vincent Quoc-Huy Trinh Rivaz, Hassan Hosseini, Mahdi S. Concordia Univ Dept Comp Sci & Software Engn CSSE Montreal PQ Canada Univ Montreal Inst Res Immunol & Canc Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn ECE Montreal PQ Canada

ISBN: (纸本)9798350365474

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim's enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim's capabilities, which reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This alignment with human expert analysis highlights Vim's potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https: //***/AtlasAnalyticsLab/Vim4Path.

关键词： Cancer Diagnosis Computational Pathology Self-Supervised Learning vision Mamba Whole Slide Image

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Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

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

作者： Praveen, R. Gnana Alam, Jahangir Comp Res Inst Montreal CRIM Montreal PQ Canada

ISBN: (纸本)9798350365474

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and intermodal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https: //***/praveena2j/RJCMA.

关键词： Concordance Correlation Coefficient Cross-Attention Dimensional Emotion recognition Joint Respresentation Multimodal Fusion Recursive Fusion

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Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

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

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9798350365474

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset's specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： action recognition

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

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

作者： Wang, Haoxiang Vasu, Pavan Kumar Anasosalu Faghri, Fartash Vemulapalli, Raviteja Farajtabar, Mehrdad Mehta, Sachin Rastegari, Mohammad Tuzel, Oncel Pouransari, Hadi Apple Cupertino CA 95014 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350365474

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multi-task learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： CLIP Foundation Model Model Merging Segmentation

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Beyond the Screen: Evaluating Deepfake Detectors under Moire pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moire...

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

作者： Tariq, Razaib Heo, Minji Woo, Simon S. Tariq, Shahroz Sungkyunkwan Univ Seoul South Korea CSIROs Data61 Eveleigh Australia

ISBN: (纸本)9798350365474

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moire patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moire patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moire patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moire patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moire patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moire pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https: //***/Razaib-Tariq/deepmoire

关键词： Deepfake Fake Media Media Forensics Moire pattern

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AAPL: Adding Attributes to Prompt Learning for vision-Language Models

AAPL: Adding Attributes to Prompt Learning for Vision-Langua...

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

作者： Kim, Gahyeon Kim, Sohee Lee, Seokju Korea Inst Energy Technol KENTECH Naju South Korea

ISBN: (纸本)9798350365474

Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted prompts. However, the performance improvement for unseen classes is still marginal, and to tackle this problem, data augmentation has been frequently used in traditional zero-shot learning techniques. Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes. To address this problem, we propose adversarial token embedding to disentangle low-level visual augmentation features from highlevel class information when inducing bias in learnable prompts. Through our novel mechanism called "Adding Attributes to Prompt Learning", AAPL, we guide the learnable context to effectively extract text features by focusing on high-level features for unseen classes. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.

关键词： prompt learning vision language model VLMs

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