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Learning from Synthetic Human Group Activities

Learning from Synthetic Human Group Activities

引用

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

作者： Chang, Che-Jui Li, Danrui Patel, Deep Goel, Parth Zhou, Honglu Moon, Seonghyeon Sohn, Samuel S. Yoon, Sejong Pavlovic, Vladimir Kapadia, Mubbasir Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs San Jose CA USA Coll New Jersey Ewing NJ USA Roblox San Mateo CA USA

ISBN: (纸本)9798350353006

The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation, we introduce M3Act, a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine, M3Act features multiple semantic groups, highly diverse and photorealistic images, and a comprehensive set of annotations, which facilitates the learning of human-centered tasks across single-person, multi-person, and multi-group conditions. We demonstrate the advantages of M(3)Act across three core experiments. The results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably, M(3)Act improves the state-of-the-art MOTRv2 on DanceTrack dataset, leading to a hop on the leaderboard from 10(th) to 2(nd) place. Moreover, M(3)Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://***/M3Act.

关键词： group activity generation group activity recognition multi-person tracking synthetic data

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ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

ChAda-ViT : Channel Adaptive Attention for Joint Representat...

引用

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

作者： Bourriez, Nicolas Bendidi, Ihab Cohen, Ethan Watkinson, Gabriel Sanchez, Maxime Bollot, Guillaume Genovesio, Auguste Ecole Normale Super PSL Paris France Minos Biosci Paris France Synsight Eviy France

ISBN: (纸本)9798350353006

Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment. Importantly, the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB, as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field, and current solutions mostly focus on intra-channel spatial attention, often ignoring the relationship between channels, yet crucial in most biological applications. Importantly, the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study, we propose ChAda-ViT, a novel Channel Adaptive vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number, order and type of channels. We also introduce IDR-Cell100k, a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities, with a multitude of channel types, and counts varying from 1 to 10 per experiment. Our architecture, trained in a self-supervised manner, outperforms existing approaches in several biologically relevant downstream tasks. Additionally, it can be used to bridge the gap for the first time between assays with different microscopes, channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses. [GRAPHICS] .

关键词： Channel Adaptive Channel Embeddings Computational Biology computer vision Deep Learning Inter-Channel Attention Long Context Microscopy Imaging Self Supervised Learning Transformers vision Transformers

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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

引用

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

作者： Xu, Yuehuan Jiang, Shuai Cui, Zhe Su, Fei Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (纸本)9798350365474

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Action recognition Deep learning Distracted driver behavior

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Look, Listen, and Attack: Backdoor Attacks Against Video Action recognition

Look, Listen, and Attack: Backdoor Attacks Against Video Act...

引用

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

作者： Hammoud, Hasan Abed Al Kader Liu, Shuming Alkhrashi, Mohammed AlBalawi, Fahad Ghanem, Bernard KAUST Thuwal Saudi Arabia SDAIA Riyadh Saudi Arabia Taif Univ Taif Saudi Arabia

ISBN: (纸本)9798350365474

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

关键词： action recognition adversarial attacks backdoor backdoor attacks video understanding

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Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

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

作者： Kolbinger, Fiona R. He, Jiangpeng Ma, Jinge Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Class Imbalance computer-Assisted Surgery Laparoscopic Surgery Semantic Segmentation Surgical Data Science

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Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer

Recurrent Homography Estimation Using Homography-Guided Imag...

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

作者： Cao, Si-Yuan Zhang, Runmin Luo, Lun Yu, Beinan Sheng, Zehua Li, Junwei Shen, Hui-Liang Zhejiang Univ Ningbo Innovat Ctr Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350301298

We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusing mechanism in FocusFormer aggregates the intra-inter correspondence in a global -> nonlocal -> local manner. Thanks to the above strategies, RHWF ranks top in accuracy on a variety of datasets, including the challenging cross-resolution and cross-modal ones. Meanwhile, benefiting from the recurrent framework, RHWF achieves parameter efficiency despite the transformer architecture. Compared to previous state-of-the-art approaches LocalTrans and IHN, RHWF reduces the mean average corner error (MACE) by about 70% and 38.1% on the MSCOCO dataset, while saving the parameter costs by 86.5% and 24.6%. Similar to the previous works, RHWF can also be arranged in 1-scale for efficiency and 2-scale for accuracy, with the 1-scale RHWF already outperforming most of the previous methods. Source code is available at https://***/imdumpl78/RHWF.

关键词： Low-level vision

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Visual Programming: Compositional visual reasoning without training

Visual Programming: Compositional visual reasoning without t...

引用

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

作者： Gupta, Tanmay Kembhavi, Aniruddha PRIOR Allen Inst AI Seattle WA 98103 USA

ISBN: (纸本)9798350301298

We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing subroutines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform.

关键词： and reasoning language vision

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

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

作者： Min, Seongjae Yang, Junseok Lim, Sejoon Kookmin Univ Seoul South Korea

ISBN: (纸本)9798350365474

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Convolutional neural networks

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Scaling Language-Image Pre-training via Masking

Scaling Language-Image Pre-training via Masking

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

作者： Li, Yanghao Fan, Haoqi Hu, Ronghang Feichtenhofert, Christoph He, Kaiming Meta AI FAIR New York NY 10023 USA

ISBN: (纸本)9798350301298

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP [52]. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning.

关键词： and reasoning language vision

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LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

LAKE-RED: Camouflaged Images Generation by Latent Background...

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

作者： Zhao, Pancheng Xu, Peng Qin, Pengda Fan, Deng-Ping Zhang, Zhicheng Jia, Guoli Zhou, Bowen Yang, Jufeng Nankai Univ Coll Comp Sci VCIP Tianjin Peoples R China Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China Nankai Univ Coll Comp Sci DISSec Tianjin Peoples R China Nankai Int Adv Res Inst Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However, the existing camouflaged generation methods require specifying the background manually, thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper, we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge, our contributions mainly include: (1) For the first time, we propose a camouflaged generation paradigm that does not need to receive any background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation, in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly, to alleviate the task-specific challenges. Moreover, our method is not restricted to specific foreground targets or backgrounds, offering a potential for extending camouflaged vision perception to more diverse domains. (3) Experimental results demonstrate that our method outperforms the existing approaches, generating more realistic camouflage images. Our source code is released on https://***/PanchengZhao/LAKE-RED.

关键词： camouflage object detection camouflaged vision perception diffusion generative model knowledge retrieval reasoning enhancement

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