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The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

The 6th Affective Behavior Analysis in-the-wild (ABAW) Compe...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dimitrios Kollias Panagiotis Tzirakis Alan Cowen Stefanos Zafeiriou Irene Kotsia Alice Baird Chris Gagne Chunchang Shao Guanyu Hu Queen Mary University of London UK Hume AI USA Imperial College London UK Cogitat UK Xi’an Jiaotong University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ieee CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and ’other’), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems and top performing teams’ per Challenge, as well as their obtained performance. More information for the Competition can be found in: https://***/6th.

关键词： Measurement Emotion recognition computer vision Protocols Target recognition Databases conferences

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Weak Multi-View Supervision for Surface Mapping Estimation

Weak Multi-View Supervision for Surface Mapping Estimation

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

作者： Rai, Nishant Liaudanskas, Aidas Rao, Srinivas Cayon, Rodrigo Ortiz Munaro, Matteo Holzer, Stefan Fyusion Inc San Francisco CA 94105 USA Stanford Univ Stanford CA 94305 USA Fyusion San Francisco CA USA

ISBN: (纸本)9781665448994

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this problem using extensive supervision in the form of pixel-level annotations, we take advantage of the fact that pixel-level UV and mesh predictions can be combined with 3D reprojections to form consistency cycles. As a result of exploiting these cycles, we can establish a dense correspondence mapping between image pixels and the mesh acting as a self-supervisory signal, which in turn helps improve our overall estimates. Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding without the need for explicit annotations. We also propose the use of deformation fields for predictions of an instance specific mesh. Given the lack of datasets providing multiple images of similar object instances from different viewpoints, we generate and release a multi-view ShapeNet Cars and Airplanes dataset created by rendering ShapeNet meshes using a 360ffi camera trajectory around the mesh. For the human faces category, we process and adapt an existing dataset to a multi-view setup. Through experimental evaluations, we show that, at test time, our method can generate accurate variations away from the mean shape, is multi-view consistent, and performs comparably to fully supervised approaches.

关键词： Deformable models Surface reconstruction Airplanes Annotations Rendering (computer graphics) Trajectory Automobiles

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jun Yu Zhihong Wei Zhongpeng Cai Gongpeng Zhao Zerui Zhang Yongqi Wang Guochen Xie Jichao Zhu Wangyuan Zhu Qingsong Liu Jiaen Liang University of Science and Technology of China Unisound AI Technology Co. Ltd

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model’s generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： computer vision Image recognition Accuracy Face recognition conferences Genetic expression Semisupervised learning

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Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms

Dynamic-OFA: Runtime DNN Architecture Switching for Performa...

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

作者： Lou, Wei Xun, Lei Sabet, Amin Bi, Jia Hare, Jonathon Merrett, Geoff, V Univ Southampton Southampton Hants England

ISBN: (纸本)9781665448994

Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available hardware resources to DNNs can vary considerably due to other concurrently running applications. The performance requirements of the applications could also change under different scenarios. To achieve the desired performance, dynamic DNNs have been proposed in which the number of channels/layers can be scaled in real time to meet different requirements under varying resource constraints. However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic. This paper proposes Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family of sub-networks from a static OFA backbone model, and contains a runtime manager to choose different sub-networks under different runtime environments. As such, Dynamic-OFA does not need the traditional dynamic DNN training pipeline. Compared to the state-of-the-art, our experimental results using ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x (GPU) faster for similar Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency.

关键词： Training Three-dimensional displays Computational modeling Pipelines Graphics processing units computer architecture Switches

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CSCO: Connectivity Search of Convolutional Operators

CSCO: Connectivity Search of Convolutional Operators

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tunhou Zhang Shiyu Li Hsin-Pai Cheng Feng Yan Hai Li Yiran Chen ECE Department Duke University Durham NC Qualcomm AI Research San Diego CA Department of Computer Science University of Houston Houston TX

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exploring dense connectivity of convolutional operators establishes critical "synapses" to communicate feature vectors from different levels and enriches the set of transformations on computer vision applications. Yet, even with heavy-machinery approaches such as Neural Architecture Search (NAS), discovering effective connectivity patterns requires tremendous efforts due to either constrained connectivity design space or a sub-optimal exploration process induced by an unconstrained search space. In this paper, we propose CSCO, a novel paradigm that fabricates effective connectivity of convolutional operators with minimal utilization of existing design motifs and further utilizes the discovered wiring to construct high-performing ConvNets. CSCO guides the exploration via a neural predictor as a surrogate of the ground-truth performance. We introduce Graph Isomorphism as data augmentation to improve sample efficiency and propose a Metropolis-Hastings Evolutionary Search (MH-ES) to evade locally optimal architectures and advance search quality. Results on ImageNet show ∼ 0.6% performance improvement over hand-crafted and NAS-crafted dense connectivity. Our code is publicly available here.

关键词： Convolutional codes Wiring computer vision Architecture Buildings computer architecture Data augmentation

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Isolated Sign Language recognition with Multi-Scale Spatial-Temporal Graph Convolutional Networks

Isolated Sign Language Recognition with Multi-Scale Spatial-...

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

作者： Vazquez-Enriquez, Manuel Alba-Castro, Jose L. Docio-Fernandez, Laura Rodriguez-Banga, Eduardo Univ Vigo AtlanTTic Res Ctr Vigo 36310 Spain

ISBN: (纸本)9781665448994

Isolated Sign Language recognition (ISLR) fits nicely in the domain of problems that can be handled by graph-structured spatial-temporal algorithms. A recent multi-scale spatial-temporal graph convolution operator, MS-G3D, takes advantage of the semantic connectivity among non-neighbor nodes of the graph in a flexible temporal scale, which results in improved performance in classical Human Action recognition datasets. In this work, we present a solution for ISLR using a skeleton graph that includes body and finger joints and makes use of this specific property of MS-G3D, which seems crucial to capture the internal relationship among semantically connected distant nodes in sign language dynamics. To complete the analysis, we compare the results with a 3D-CNN architecture, S3D, already used for SLR, and fuse it with MS-G3D. The performance achieved on the AUTSL dataset shows that MS-G3D alone stands out as a viable technique for ISLR. In fact, the improvement after fusing with a 3D-CNN approach, at least on this medium-scale dataset, appears marginal. The transfer learning capability of the trained models is also explored using pre-training with the larger WLASL dataset and post-training with the smaller LSE_UVIGO dataset. The classification performance based on the MS-G3D model over AUTSL does not benefit from pre-training with WLASL, but the performance on the more similarly acquired LSE UVIGO dataset improves significantly from fine-tuning the MS-G3D AUTSL model.

关键词： computer vision Fuses Convolution Assistive technology Computational modeling conferences Transfer learning

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Self-supervised multi-image super-resolution for push-frame satellite images

Self-supervised multi-image super-resolution for push-frame ...

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

作者： Ngoc Long Nguyen Anger, Jeremy Davy, Axel Arias, Pablo Facciolo, Gabriele Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS Gif Sur Yvette France Kayrros SAS Paris France

ISBN: (纸本)9781665448994

Recent constellations of optical satellites are adopting multi-image super-resolution (MISR) from bursts of push-frame images as a way to increase the resolution and reduce the noise of their products while maintaining a lower cost of operation. Most MISR techniques are currently based on the aggregation of samples from registered low resolution images. A promising research trend aimed at incorporating natural image priors in MISR consists in using data-driven neural networks. However, due to the unavailability of ground truth high resolution data, these networks cannot be trained on real satellite images. In this paper, we present a framework for training MISR algorithms from bursts of satellite images without requiring high resolution ground truth. This is achieved by adapting the recently proposed frame-to-frame framework to process bursts of satellite images. In addition we propose an architecture based on feature aggregation that allows to fuse a variable number of frames and is capable of handling degenerate samplings while also reducing noise. On synthetic datasets, the proposed self-supervision strategy attains results on par with those obtained with a supervised training. We applied our framework to real SkySat satellite image bursts leading to results that are more resolved and less noisy than the L1B product from Planet.

关键词： Training Satellites Planets Superresolution Neural networks computer architecture Optical imaging

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Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Omkar Prabhune Tianen Chen Younghyun Kim Purdue University University of Wisconsin-Madison

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication *** this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a content-aware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： Deep learning Training Radio frequency Visualization Accuracy Machine vision Face recognition

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Sharpness-Aware Optimization for Real-World Adversarial Attacks for Diverse Compute Platforms with Enhanced Transferability

Sharpness-Aware Optimization for Real-World Adversarial Atta...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Muchao Ye Xiang Xu Qin Zhang Jonathan Wu Amazon AI Labs Seattle WA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, deep neural networks (DNNs) have become integral to many real-world applications. A pressing concern in these deployments pertains to their vulnerability to adversarial attacks. In this work, we focus on the transferability of adversarial examples in a real-world deployment setting involving both a cloud model and an edge model. The cloud model is a black-box victim model, while the edge model is a surrogate model that is fully accessible to users. We investigated scenarios where attackers leverage information from the known surrogate model to generate adversarial examples to attack the unknown black-box victim model. Existing methods often optimize the adversarial example generation based on the steepest gradients estimated from the surrogate model, which do not generalize effectively to the victim model. To better gauge the for real-world adversarial risks in a cloud-edge deployment setting, we proposed an novel attack mechanism that enhanced transferability by incorporating a sharpness-aware objective into the optimization process. Our evaluation on image classification benchmarks demonstrates that our method significantly improves adversarial example’s transferability, even on the foundational computer vision models such as OFA-Large, showcasing its potential as a new standard in assessing attack transferability within a cloud-edge hybrid deployment scenario.

关键词： computer vision Computational modeling Perturbation methods Closed box Artificial neural networks Pressing pattern recognition

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Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Fast Camera Image Denoising on Mobile GPUs with Deep Learnin...

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

作者： Ignatov, Andrey Byeoung-Su, Kim Timofte, Radu Pouget, Angeline Song, Fenglong Li, Cheng Xiao, Shuai Fu, Zhongqian Maggioni, Matteo Huang, Yibin Cheng, Shen Lu, Xin Zhou, Yifeng Chen, Liangyu Liu, Donghao Zhang, Xiangyu Fan, Haoqiang Sun, Jian Liu, Shuaicheng Kwon, Minsu Lee, Myungje Yoo, Jaeyoon Kang, Changbeom Wang, Shinjo Huang, Bin Zhou, Tianbao Liu, Shuai Lei, Lei Feng, Chaoyu Huang, Liguang Lei, Zhikun Chen, Feifei Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Samsung Elect Suwon South Korea AI Witchlabs Zollikerberg Switzerland Huawei Noahs Ark Lab Hong Kong Peoples R China Megvii Beijing Peoples R China ENERZAi Seoul South Korea Xiaomi AI Lab Beijing Peoples R China North China Univ Technol Beijing Peoples R China Xiaomi Inc Beijing Peoples R China Goodix Technol Algorithm Dept Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Runtime Image resolution Neural networks Graphics processing units Mobile handsets pattern recognition Noise measurement

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