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An Effective Ensemble Learning Framework for Affective Behaviour Analysis

An Effective Ensemble Learning Framework for Affective Behav...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wei Zhang Feng Qiu Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional behavior analysis techniques in natural environments, the 6th competition on Affective Behavior Analysis in-the-wild (ABAW) utilizes the Aff-Wild2, Hume-Vidmimic2, and C-EXPR-DB datasets to set up five competitive tracks, i.e., Valence-Arousal (VA) Estimation, Expression (EXPR) recognition, Action Unit (AU) Detection, Compound Expression (CE) recognition, and Emotional Mimicry Intensity (EMI) Estimation. In this paper, we present our method designs for VA estimation, expression recognition, and AU detection tracks. Specifically, our framework mainly includes three aspects: 1) To achieve high-quality facial feature representations, we employ Masked-Auto Encoder as the visual features extraction model and fine-tune it with our facial dataset. 2) Utilizing a transformer-based feature fusion module to fully integrate emotional information provided by audio signals, visual images, and transcripts, offering high-quality expression features for the downstream tasks. 3) Considering the complexity of the video collection scenes, we conduct a more detailed dataset division based on scene characteristics and train the classifier for each scene. Extensive experiments demonstrate the superiority of our designs. Our work won the championship in the AU, EXPR, and VA tracks at the ABAW6 competition.

关键词： Human computer interaction Gold Visualization Emotion recognition Design methodology Estimation Feature extraction

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Dealing with Uncertainty in the Principal Directions of Tensors

Dealing with Uncertainty in the Principal Directions of Tens...

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Radar conference, 2005 IEEE International;Anchorage,AK,USA

作者： Maxime Boucher Alan Evans School of Computer Science McGill University Montreal Canada McConnell Brain Imaging Center McGill University Montreal Canada

The variability of tensor fields is usually analyzed with multivariate statistical distributions. Multivariate distributions model every component of the tensor, which are not invariant under rotation. They therefore tell very little information about the true shape of the tensor. A statistical analysis on the eigenvalues of the tensor would be more revealing. The eigenvalues determine if a tensor is uniaxial, i.e.: only one eigenvalue is different from zero, isotropic, volume preserving or purely anisotropic. However, the eigenvalues of a normally distributed tensor are not, in general, normally distributed. In this paper, we solve this problem directly for small sizes of samples by determining the probability that the maximum error is within a reasonable bound. When the error is likely to be within a reasonable bound, we consider the eigenvalues of a tensor to be normally distributed along a mean eigendirection. Monte Carlo simulation shows that the computed bound is tight and becomes tighter when the number of sample increases. An application of the method, analysis of deformations on the cortical surface, is presented in this paper. On this data, we found that 80% of the anisotropic deformations could be analyzed by modeling the eigenvalues directly. Thus, the proposed method allows formulating statistical hypothesis directly on eigenvalues in many cases of measured deformations. Although the method was used in only one application, the method could be extended to application involving diffusion MRI or other imaging technique involving tensors.

关键词： Uncertainty Tensile stress Eigenvalues and eigenfunctions Anisotropic magnetoresistance Statistical distributions Shape Statistical analysis Deformable models Magnetic resonance imaging Diffusion tensor imaging

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Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Farzaneh Askari Cyril Yared Rohit Ramaprasad Devin Garg Anjun Hu James J. Clark University of McGill Montreal QC Canada University of California San Diego San Diego CA United States University of Oxford Oxford United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Adaptation models computer architecture Transformers Skeleton Graph neural networks Data models Recording

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OpenStory: A Large-Scale Open-Domain Dataset for Subject-Driven Visual Storytelling

OpenStory: A Large-Scale Open-Domain Dataset for Subject-Dri...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zilyu Ye Jinxiu Liu JinJin Cao Zhiyang Chen Ziwei Xuan Mingyuan Zhou Qi Liu Guo-Jun Qi School of Future Technology South China University of Technology Westlake University Foundation Model Research Center CASIA OPPO US Research Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, the advancement and evolution of generative AI have been highly compelling. In this paper, we present OpenStory, a large-scale dataset tailored for training subject-focused story visualization models to generate coherent and contextually relevant visual narratives. Addressing the challenges of maintaining subject continuity across frames and capturing compelling narratives, We propose an innovative pipeline that automates the extraction of keyframes from open-domain videos. It ingeniously employs vision-language models to generate descriptive captions, which are then refined by a large language model to ensure narrative flow and coherence. Furthermore, advanced subject masking techniques are applied to isolate and segment the primary subjects. Derived from diverse video sources, including YouTube and existing datasets, OpenStory offers a comprehensive open-domain resource, surpassing prior datasets confined to specific scenarios. With automated captioning instead of manual annotation, high-resolution imagery optimized for subject count per frame, and extensive frame sequences ensuring consistent subjects for temporal modeling, OpenStory establishes itself as an invaluable benchmark. It facilitates advancements in subject-focused story visualization, enabling the training of models capable of comprehending and generating intricate multi-modal narratives from extensive visual and textual inputs.

关键词： Training Visualization computer vision Annotations Large language models Pipelines Manuals

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A saliency-based technique for advertisement layout optimisation to predict customers’ behaviour 25th

A saliency-based technique for advertisement layout optimisa...

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25th International conference on pattern recognition workshops, ICPR 2020

作者： Bruno, Alessandro Lancette, Stéphane Zhang, Jinglu Moore, Morgan Ward, Ville P. Chang, Jian National Centre for Computer Animation Bournemouth University PooleBH12 5BB United Kingdom Shoppar Ltd. Plexal 14 East Bay Lane Stratford LondonE20 3BS United Kingdom

ISBN: (纸本)9783030687892

Customer retail environments represent an exciting and challenging context to develop and put in place cutting-edge computer vision techniques for more engaging customer experiences. Visual attention is one of the aspects that play such a critical role in the analysis of customers behaviour on advertising campaigns continuously displayed in shops and retail environments. In this paper, we approach the optimisation of advertisement layout content, aiming to grab the audience’s visual attention more effectively. We propose a fully automatic method for the delivery of the most effective layout content configuration using saliency maps out of each possible set of images with a given grid layout. Visual Saliency deals with the identification of the most critical regions out of pictures from a perceptual viewpoint. We want to assess the feasibility of saliency maps as a tool for the optimisation of advertisements considering all possible permutations of images which compose the advertising campaign itself. We start by analysing advertising campaigns consisting of a given spatial layout and a certain number of images. We run a deep learning-based saliency model over all permutations. Noticeable differences among global and local saliency maps occur over different layout content out of the same images. The latter aspect suggests that each image gives its contribution to the global visual saliency because of its content and location within the given layout. On top of this consideration, we employ some advertising images to set up a graphical campaign with a given design. We extract relative variance values out the local saliency maps of all permutations. We hypothesise that the inverse of relative variance can be used as an Effectiveness Score (ES) to catch those layout content permutations showing the more balanced spatial distribution of salient pixel. A group of 20 participants have run some eye-tracking sessions over the same advertising layouts to validate the proposed

关键词： computer vision

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Masked Autoencoders are Secretly Efficient Learners

Masked Autoencoders are Secretly Efficient Learners

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zihao Wei Chen Wei Jieru Mei Yutong Bai Zeyu Wang Xianhang Li Hongru Zhu Huiyu Wang Alan Yuille Yuyin Zhou Cihang Xie University of Michigan Ann Arbor Johns Hopkins University UC Santa Cruz Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper provides an efficiency study of training Masked Autoencoders (MAE), a framework introduced by He et al. [13] for pre-training vision Transformers (ViTs). Our results surprisingly reveal that MAE can learn at a faster speed and with fewer training samples while maintaining high performance. To accelerate its training, our changes are simple and straightforward: in the pre-training stage, we aggressively increase the masking ratio, decrease the number of training epochs, and reduce the decoder depth to lower the pre-training cost; in the fine-tuning stage, we demonstrate that layer-wise learning rate decay plays a vital role in unlocking the full potential of pre-trained models. Under this setup, we further verify the sample efficiency of MAE: training MAE is hardly affected even when using only 20% of the original training *** combining these strategies, we are able to accelerate MAE pre-training by a factor of 82 or more, with little performance drop. For example, we are able to pre-train a ViT-B in ~9 hours using a single NVIDIA A100 GPU and achieve 82.9% top-1 accuracy on the downstream ImageNet classification task. Additionally, we also verify the speed acceleration on another MAE extension, SupMAE.

关键词： Training computer vision Costs conferences Computational modeling Graphics processing units Transformers

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Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Improving Object Detection to Fisheye Cameras with Open-Voca...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Long Hoang Pham Quoc Pham-Nam Ho Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Huy-Hung Nguyen Duong Khac Vu Chi Dai Tran Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model’s hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.

关键词： Training computer vision Adaptation models Vocabulary Roads Urban areas Object detection

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AdvDenoise: Fast Generation Framework of Universal and Robust Adversarial Patches Using Denoise

AdvDenoise: Fast Generation Framework of Universal and Robus...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jing Li Zigan Wang Jinliang Li School of Economics and Management Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Adversarial patch attacks, which can mislead deep learning models and the human eye in both the digital and physical domains, have led to a trust crisis. Traditional approaches to generating powerful attack patches require extensive, multi-scenario data, but suffer from slow search speeds in adversarial gradient space, resulting in low global attack success rates and high costs. Especially high resource-consuming attack methods are not sufficient to pose sufficient threats, which leads to the vulnerability of defense. To address these challenges, we present a novel framework AdvDenoise to generate universal adversarial patches fast and robustly using denoise. Concretely, we leverage the power of denoising diffusion probabilistic models to craft or optimize these patches, deviating from traditional pure gradient-based methods. We conduct comprehensive experiments on both pre-trained convolutional neural networks and vision transformer detectors, evaluating our method on standard benchmarks as well as in simulated real-world physical settings. The results demonstrate that our framework outperforms strong baselines, achieving higher attack success rates, better transferability across models, and improved robustness to transformations while maintaining visual realism and computational efficiency. When our method’s performance approaches the state-of-the-art, the total time required to generate 100-shots adversarial patches is substantially lower than the state-of-the-art methods, with a remarkable 48.15% reduction in time complexity. The code and examples are publicly available at https://***/advdenoise/advdenoise.

关键词： computer vision Visualization Computational modeling Noise reduction Diffusion models Transformers Robustness

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Gait recognition Through MPCA Plus LDA

Gait Recognition Through MPCA Plus LDA

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Biometrics Symposium

作者： Haiping Lu K.N. Plataniotis A.N. Venetsanopoulos The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Canada

This paper solves the gait recognition problem in a multilinear principal component analysis (MPCA) framework. Gait sequences are naturally described as tensor objects and feature extraction for tensor objects is important in computer vision and pattern recognition applications. Classical principal component analysis (PCA) operates on vectors and it is not directly applicable to gait sequences. This work introduces an MPCA framework for feature extraction from gait sequences by seeking a multilinear projection onto a tensor subspace of lower dimensionality which captures most of the variance of the original gait samples. A subset of the extracted eigen-tensors are selected and the classical LDA is then applied. In experiments, gait recognition results are reported on the Gait Challenge data sets using the proposed solution. The results indicate that with a simple design, the proposed algorithm outperforms the state-of-the-art algorithms.

关键词： Linear discriminant analysis Tensile stress Principal component analysis Feature extraction Biometrics Data mining Fingerprint recognition Strontium computer vision pattern recognition

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ReweightOOD: Loss Reweighting for Distance-based OOD Detection

ReweightOOD: Loss Reweighting for Distance-based OOD Detecti...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sudarshan Regmi Bibek Panthi Yifei Ming Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal University of Wisconsin-Madison USA West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Out-of-Distribution (OOD) detection is crucial for ensuring safety and reliability of neural networks in critical applications. Distance-based OOD detection is based on the assumption that OOD samples are mapped far from In-Distribution (ID) clusters in embedding space. A recent approach for obtaining OOD-detection-friendly embedding space has been contrastive optimization of pulling similar pairs and pushing apart dissimilar pairs. It assigns equal significance to all similarity instances with the implicit objective of maximizing the mean proximity between samples with their corresponding hypothetical class centroids. However, the emphasis should be directed towards reducing the Minimum Enclosing Sphere (MES) for each class and achieving higher inter-class dispersion to effectively mitigate the potential for ID-OOD overlap. Optimizing low-signal dissimilar pairs might potentially act against achieving maximal inter-class dispersion while less-optimized similar pairs prevent achieving smaller MES. Based on this, we propose a reweighting scheme ReweightOOD, that adopts the similarity optimization which prioritizes the optimization of less-optimized contrasting pairs while assigning lower importance to already well-optimized contrasting pairs. Such a reweighting scheme serves to minimize the MES for each class while achieving maximal interclass dispersion. Experimental results on a challenging CIFAR100 benchmark using ResNet-18 network demonstrate that ReweightOOD outperforms supervised contrastive loss by a whopping 38% in the average FPR metric. In various classification datasets, our method provides a promising solution for enhancing OOD detection capabilities in neural networks.

关键词： Measurement computer vision conferences Neural networks Benchmark testing Safety pattern recognition

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