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CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

CycleGANAS: Differentiable Neural Architecture Search for Cy...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Taegun An Changhee Joo Department of Computer Science and Engineering Korea University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully addresses the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures. Our code is at https://***/antaegun20/CycleGANAS. 1

关键词： Dimensionality reduction Search methods Stacking Neural networks computer architecture Generative adversarial networks Generators

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NeighborTrack: Single Object Tracking by Bipartite Matching with Neighbor Tracklets and Its Applications to Sports

NeighborTrack: Single Object Tracking by Bipartite Matching ...

引用

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

作者： Chen, Yu-Hsi Wang, Chien-Yao Yang, Cheng-Yun Chang, Hung-Shuo Lin, Youn-Long Chuang, Yung-Yu Liao, Hong-Yuan Mark Institute of Information Science Academia Sinica Taiwan Purdue University Department of Electrical Engineering United States National Tsing Hua University Department of Computer Science Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350302493

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of 1.92% EAO and 2.11% robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art 72.25% AUC on LaSOT and 75.7% AO on GOT-10K. Most of the tracking examples we have used are related to sports. © 2023 ieee.

关键词： Target tracking

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Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models

Reliable Trajectory Prediction and Uncertainty Quantificatio...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marion Neumeier Sebastian Dorn Michael Botsch Wolfgang Utschick Technische Hochschule Ingolstadt Audi AG Technische Hochschule Augsburg Technische Universität München

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. The proposed model ensures the drivability of the predicted trajectory by integrating non-holonomic motion constraints and physical constraints into the generative prediction module. Central to the architecture of cVMD is its capacity to perform uncertainty quantification, a feature that is crucial in safety-critical applications. By integrating the quantified uncertainty into the prediction process, the cVMD’s trajectory prediction performance is improved considerably. The model’s performance was evaluated using the publicly available highD dataset. Experiments show that the proposed architecture achieves competitive trajectory prediction accuracy compared to state-of-the-art models, while providing guaranteed drivable trajectories and uncertainty quantification.

关键词： Road transportation Uncertainty computer architecture Predictive models Network architecture Diffusion models Real-time systems

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Uncertainty Estimation for Tumor Prediction with Unlabeled Data

Uncertainty Estimation for Tumor Prediction with Unlabeled D...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Juyoung Yun Shahira Abousamra Chen Li Rajarsi Gupta Tahsin Kurc Dimitris Samaras Alison Van Dyke Joel Saltz Chao Chen Department of Computer Science Stony Brook University USA Department of Biomedical Informatics Stony Brook University USA National Cancer Institute Maryland USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper, we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology, we propose to adopt novel learning method that can fully exploit unlabeled data. The proposed method achieves superior performance compared with different baselines including the celebrated Monte-Carlo Dropout. Closeup inspection of uncertain regions reveal insight into the model and improves the trustworthiness of the models.

关键词： Pathology Uncertainty Accuracy Monte Carlo methods Neural networks Estimation Predictive models

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Multi-scale Attention-Based Inclination Angles Estimation for Panoramic Camera

Multi-scale Attention-Based Inclination Angles Estimation fo...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yuhao Shan Heyu Chen Jiaying Zhang Shigang Li Jianfeng Li Southwest University Chongqing China Hiroshima City University Hiroshima Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Images taken by panoramic cameras in the upright posture can give viewers a better sense and make the downstream panoramic image-based computer vision tasks easier. To estimate the inclination angles of panoramic camera, we proposed a simple but elegant panoramic image-based network, which combines the advantages of geometry-based and deep-learning-based methods. First, a backbone network with five down-sampling layers is designed to focus on the local distortion features. Then, since non-upright panoramic images have highly uniform geometric distortion for the same camera inclination angles, a multi-scale attention module is proposed for the first time, which can weigh each pixel on the feature maps of the backbone network and allows the network to focus on the global and shallow geometric features. Moreover, apart from angle loss, pixel-level image loss is introduced in our network for the inclination angles estimation task to allow the network to compensate for pixel deviations during training. The experiments show that our method overcomes other leading state-of-the-art methods in this field.

关键词： Training computer vision conferences Estimation Cameras Distortion pattern recognition

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Augmented Self-Mask Attention Transformer for Naturalistic Driving Action recognition

Augmented Self-Mask Attention Transformer for Naturalistic D...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tiantian Zhang Qingtian Wang Xiaodong Dong Wenqing Yu Hao Sun Xuyang Zhou Aigong Zhen Shun Cui Dong Wu Zhongjiang He China Telecom Artificial Intelligence Technology (Beijing) Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, naturalistic driving action recognition and computer vision techniques provide crucial solutions to identify and eliminate distracting driving behavior. Existing methods often extract features through fixed-size sliding windows and predict an action’s start and end time. However, the information about a fixed-size window may be incomplete or redundant and the connections between different windows are insufficient. To alleviate this problem, we propose a novel Augmented Self-Mask Attention (AMA) architecture that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order. We employ an ensemble technique and use a weighted boundaries fusion to combine and refine predictions with high confidence scores action boundaries. On the test dataset of AI City Challenge 2024 Track3, we achieved significant results compared with other teams, the proposed model ranks first on the public leaderboard of the challenge. Codes are available at https://***/wolfworld6/AIcity2024-track3.

关键词： computer vision conferences Computational modeling Urban areas computer architecture Feature extraction Transformers

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Towards Online Real-Time Memory-based Video Inpainting Transformers

Towards Online Real-Time Memory-based Video Inpainting Trans...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Guillaume Thiry Hao Tang Radu Timofte Luc Van Gool CVL ETH Zurich Carnegie Mellon University University of Wurzburg ESAT-PSI KU Leuven INSAIT Sofia Un. St. Kliment Ohridski

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers. Although these models show promising reconstruction quality and temporal consistency, they are still unsuitable for live videos, one of the last steps to make them completely convincing and usable. The main limitations are that these state-of-the-art models inpaint using the whole video (offline processing) and show an insufficient frame rate. In our approach, we propose a framework to adapt existing inpainting transformers to these constraints by memorizing and refining redundant computations while maintaining a decent inpainting quality. Using this framework with some of the most recent inpainting models, we show great online results with a consistent throughput above 20 frames per second.

关键词： computer vision Adaptation models Computational modeling conferences Refining Streaming media Transformers

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Video Analytics for Detecting Motorcyclist Helmet Rule Violations

Video Analytics for Detecting Motorcyclist Helmet Rule Viola...

引用

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

作者： Tsai, Chun-Ming Hsieh, Jun-Wei Chang, Ming-Ching He, Guan-Lin Chen, Ping-Yang Chang, Wei-Tsung Hsieh, Yi-Kuan University of Taipei Department of Computer Science Taipei10048 Taiwan National Yang Ming Chiao Tung University College of Ai and Green Energy Tainan71150 Taiwan University at Albany State University of New York Department of Computer Science NY12222 United States

ISBN: (纸本)9798350302493

The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://***/cmtsai2023/AICITY2023-Track5-DVHRM. © 2023 ieee.

关键词： Object detection

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Seongjae Min Junseok Yang Sejoon Lim Kookmin University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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： Deep learning Emotion recognition Gold computer vision Computational modeling Estimation Transformer cores

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PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet La...

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

作者： Wang, Bor-Shiun Chen, Ping-Yang Hsieh, Yi-Kuan Hsieh, Jun-Wei Chang, Ming-Ching He, JiaXin Teng, Shin-You Yue, HaoYuan Tseng, Yu-Chee National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yang Ming Chiao Tung University College of Ai and Green Energy Tainan71150 Taiwan University at Albany State University of New York Department of Computer Science NY12222 United States Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798350302493

We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://***/NYCU-AICVLab/AICITY-2023-Track5. © 2023 ieee.

关键词： Object detection

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