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ScanpathNet: A Recurrent Mixture Density Network for Scanpath Prediction

ScanpathNet: A Recurrent Mixture Density Network for Scanpat...

引用

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

作者： de Belen, Ryan Anthony Jalova Bednarz, Tomasz Sowmya, Arcot Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the mechanisms underlying human visual attention is an important research problem in cognitive neuroscience and computer vision. While existing models predict salient regions (i.e., saliency maps) and temporal sequences of eye fixations (i.e., scanpaths) in images, their designs often partially follow theoretical frameworks. Here, we introduce ScanpathNet, a deep learning model inspired by the latest theoretical model in neuroscience. It is 'guided' by a dynamic priority map influenced by semantic content and fixation history. The model leverages convolutional neural networks to extract rich semantic features, convolutional long short-term memory networks to model the inhibition of return mechanism and sequential dependencies of fixations, and mixture density networks to predict probability distributions of fixations for each pixel. Simulated human scanpaths can then be generated by sequentially sampling the output of the proposed model. Despite its simplicity, ScanpathNet showed promising qualitative and quantitative scanpath prediction performance in extensive experiments on numerous eye-tracking benchmark datasets.

关键词： Deep learning Visualization computer vision Computational modeling Semantics Predictive models Visual systems

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MoPE-CLIP: Structured Pruning for Efficient vision-Language Models with Module-wise Pruning Error Metric

MoPE-CLIP: Structured Pruning for Efficient Vision-Language ...

引用

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

作者： Lin, Haokun Bai, Haoli Liu, Zhili Hou, Lu Sung, Muyi Song, Linqi Wei, Ying Sun, Zhenan Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci CRIPAC Beijing Peoples R China Chinese Acad Sci MAIS Inst Automat Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China City Univ Hong Kong Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with limited computational resources. We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance. Recent efforts for VLP compression either adopt uni-modal compression metrics resulting in limited performance or involve costly mask-search processes with learnable masks. In this paper, we first propose the Module-wise Pruning Error (MoPE) metric, accurately assessing CLIP module importance by performance decline on cross-modal tasks. Using the MoPE metric, we introduce a unified pruning framework applicable to both pre-training and task-specific fine-tuning compression stages. For pre-training, MoPE-CLIP effectively leverages knowledge from the teacher model, significantly reducing pre-training costs while maintaining strong zero-shot capabilities. For fine-tuning, consecutive pruning from width to depth yields highly competitive task-specific models. Extensive experiments in two stages demonstrate the effectiveness of the MoPE metric, and MoPE-CLIP outperforms previous state-of-the-art VLP compression methods.

关键词： CLIP Model Compression Structured Pruning vision-Language Models

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Federated Learning-based Driver Activity recognition for Edge Devices

Federated Learning-based Driver Activity Recognition for Edg...

引用

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

作者： Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video action recognition has been an active area of research for the past several years. However, the majority of research is concentrated on recognizing a diverse range of activities in distinct environments. On the other hand, Driver Activity recognition (DAR) is significantly more difficult since there is a much finer distinction between various actions. Moreover, training robust DAR models requires diverse training data from multiple sources, which might not be feasible for a centralized setup due to privacy and security concerns. Furthermore, it is critical to develop efficient models due to limited computational resources available on vehicular edge devices. Federated Learning (FL), which allows data parties to collaborate on machine learning models while preserving data privacy and reducing communication requirements, can be used to overcome these challenges. Despite significant progress on various computer vision tasks, FL for DAR has been largely unexplored. In this work, we propose an FL-based DAR model and extensively benchmark the model performance on two datasets under various practical setups. Our results indicate that the proposed approach performs competitively under the centralized (non-FL) and decentralized (FL) settings.

关键词： Performance evaluation Data privacy computer vision Computational modeling Activity recognition Collaborative work Data models

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CORE: Consistent Representation Learning for Face Forgery Detection

CORE: Consistent Representation Learning for Face Forgery De...

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

作者： Ni, Yunsheng Meng, Depu Yu, Changqian Quan, Chengbin Ren, Dongchun Zhao, Youjian Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Meituan Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods. Our code is available at https://***/niyunsheng/CORE.

关键词： Representation learning computer vision Codes Face recognition conferences Market research Forgery

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Implications of Solution patterns on Adversarial Robustness

Implications of Solution Patterns on Adversarial Robustness

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

作者： Liang, Hengyue Liang, Buyun Sun, Ju Cui, Ying Mitchell, Tim University of Minnesota Department of Electrical & Computer Engineering United States University of Minnesota Department of Computer Science & Engineering United States University of Minnesota Department of Industrial & Systems Engineering United States City University of New York Queens College Department of Computer Science United States

ISBN: (纸本)9798350302493

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations involves solving non-trivial constrained optimization problems. Recent work has shown that these RE problems can be reliably solved by a general-purpose constrained-optimization solver, PyGRANSO with Constraint-Folding (PWCF). In this paper, we take advantage of PWCF and other existing numerical RE algorithms to explore distinct solution patterns in solving RE problems with various combinations of losses, perturbation models, and optimization algorithms. We then provide extensive discussions on the implications of these patterns on current robustness evaluation and adversarial training. A comprehensive version of this work can be found in [19]. © 2023 ieee.

关键词： Constrained optimization

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EKILA: Synthetic Media Provenance and Attribution for Generative Art

EKILA: Synthetic Media Provenance and Attribution for Genera...

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

作者： Balan, Kar Agarwal, Shruti Jenni, Simon Parsons, Andy Gilbert, Andrew Collomosse, John University of Surrey United Kingdom Adobe Inc.

ISBN: (纸本)9798350302493

We present EKILA;a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance - determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI. © 2023 ieee.

关键词： computer vision

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MMVP: A Multimodal MoCap Dataset with vision and Pressure Sensors

MMVP: A Multimodal MoCap Dataset with Vision and Pressure Se...

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

作者： Zhang, He Ren, Shenghao Yuan, Haolei Zhao, Jianhui Li, Fan Sun, Shuangpeng Liang, Zhenghao Yu, Tao Shen, Qiu Cao, Xun Beihang Univ Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Weilan Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350353006

Foot contact is an important cue for human motion capture, understanding, and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However, these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion, as well as accurate and dense foot-contact annotation. To fill this gap, we propose a Multimodal MoCap Dataset with vision and Pressure sensors, named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations, which is especially useful for both plausible shape estimation, robust pose fitting without foot drifting, and accurate global translation tracking. To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://***/MMVP-Dataset/

关键词： foot contact Motion capture multimodal pressure

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Semi-Supervised Training to Improve Player and Ball Detection in Soccer

Semi-Supervised Training to Improve Player and Ball Detectio...

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

作者： Vandeghen, Renaud Cioppa, Anthony Van Droogenbroeck, Marc Univ Liege Liege Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%.

关键词： Training Adaptation models Annotations Benchmark testing Data models pattern recognition Proposals

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Live Demonstration: PINK: Polarity-based Anti-flicker for Event Cameras

Live Demonstration: PINK: Polarity-based Anti-flicker for Ev...

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

作者： Im, Gyubeom Park, Keunjoo Kim, Junseok Son, Bongki Shin, Seungchul Lee, Haechang Samsung Electronics 1-1 Samsungjeonja-ro Hwaseong-Si Korea Republic of

ISBN: (纸本)9798350302493

Dynamic vision Sensor (DVS) is an event sensor that asynchronously captures an event whenever there is a brightness change in the scene. However, due to the event sensor's high temporal resolution property, it is particularly vulnerable to flicker. Numerous unwanted events caused by flickering can cause serious loads on computing power and prevent the event sensor from extracting useful information, making it difficult to use the event sensor for machine vision applications in practice. In this demo, we propose the PINK (PolarIty-based aNti-flicKer), which filters flicker based on the event polarity (on/off). The proposed method is lightweight and efficient and can be used in real time. To the best of our knowledge, this is the first anti-flicker algorithm that works in real time and provides a usable opticalflow result for machine vision applications in an extreme flicker environment. A video showing the example of live demo is available online at https://***/5UdU0PLZaf8. © 2023 ieee.

关键词： computer vision

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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised Domain Adaptation for Cardiac Segmentation: Tow...

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

作者： Lu, Changjie Zheng, Shen Gupta, Gaurav Wenzhou Kean Univ Coll Sci & Technol Wenzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively.

关键词： Image segmentation Adaptation models conferences Estimation Benchmark testing pattern recognition Task analysis

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