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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

共 6849 条记录，以下是251-260 订阅

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Stargazer: A Transformer-based Driver Action Detection System for Intelligent Transportation

Stargazer: A Transformer-based Driver Action Detection Syste...

引用

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

作者： Liang, Junwei Zhu, He Zhang, Enwei Zhang, Jun Tencent Youtu Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection using videos captured inside the vehicle. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information, with a simple yet effective action temporal localization framework. The core of our system contains an improved version of the multi-scale vision transformer network, which learns a hierarchy of robust representations. We then use a slidingwindow classification strategy to facilitate temporal localization of actions-of-interest. The proposed system wins the second place in the Naturalistic Driving Action recognition of AI City Challenge 2022 (Track 3)(1). The code and models are released(2).

关键词： Location awareness Roads Urban areas Transformer cores Transformers pattern recognition Behavioral sciences

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Neural Face Video Compression using Multiple Views

Neural Face Video Compression using Multiple Views

引用

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

作者： Volokitin, Anna Brugger, Stefan Benlalah, Ali Martin, Sebastian Amberg, Brian Tschannen, Michael Swiss Fed Inst Technol Zurich Switzerland Apple Cupertino CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent advances in deep generative models led to the development of neural face video compression codecs that use an order of magnitude less bandwidth than engineered codecs. These neural codecs reconstruct the current frame by warping a source frame and using a generative model to compensate for imperfections in the warped source frame. Thereby, the warp is encoded and transmitted using a small number of keypoints rather than a dense flow field, which leads to massive savings compared to traditional codecs. However, by relying on a single source frame only, these methods lead to inaccurate reconstructions (e.g. one side of the head becomes unoccluded when turning the head and has to be synthesized). Here, we aim to tackle this issue by relying on multiple source frames (views of the face) and present encouraging results.

关键词： Codecs Uncertainty Computational modeling Face recognition Video compression Streaming media Real-time systems

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Domain Adaptable Normalization for Semi-Supervised Action recognition in the Dark

Domain Adaptable Normalization for Semi-Supervised Action Re...

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

作者： Liang, Zixi Chen, Jiajun Chen, Rui Zheng, Bingbing Zhou, Mingyue Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Action recognition in the dark is gaining more and more attention with the rapid development of intelligent recognition applications in real-world applications, e.g. self-driving at night and night surveillance. However, limited by the expensive labeling cost, it is impractical to produce a large-scale labeled dataset only for dark environments. Therefore, a practical solution adopted is to transfer models trained from clear environments to dark environments through semi-supervised learning. However, prior works rely heavily on additional efforts such as extra annotations, or extra sensors. To this end, we proposed a novel and simple Domain Adaptable Normalization (DANorm) method to align different domains directly, which consists of feature normalization, angle constraint and the Pseudo-Label. Specifically, the proposed DANorm method enables the model automatically learning the associated features between labeled source domain and unlabeled target domain by constraining the feature subspace vectors. Experimental results show that our model achieves superiority performance on Semi-supervised ARID dataset.

关键词： Adaptation models computer vision Costs Surveillance conferences Semisupervised learning pattern recognition

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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

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

作者： Ranem, Amin Gonzalez, Camila Mukhopadhyay, Anirban Tech Univ Darmstadt GRIS Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (纸本)9781665487399

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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Constellations: A novel dataset for studying iterative inference in humans and AI

Constellations: A novel dataset for studying iterative infer...

引用

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

作者： Khajuria, Tarun Tulver, Kadi Luik, Taavi Aru, Jaan Univ Tartu Inst Comp Sci Tartu Estonia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Under complex viewing conditions, human perception relies on generating hypotheses and revising them in an iterative fashion. We developed novel visual stimuli to study such iterative inference in humans and AI. In these stimuli, called "constellations", all local information about the object has been removed and it can only be recognized when taking into account the global pattern. We here describe the dataset and demonstrate that humans indeed use an iterative process of generating hypotheses and refining them to solve these images. We also provide code that allows researchers to create their own constellation images. The constellation dataset allows researchers to develop sketching algorithms for guessing the hidden object. As such algorithms used by humans appear to be iterative in nature, this dataset will facilitate the study of iterative inference in minds and machines.

关键词： Visualization Image recognition Machine vision conferences Refining Iterative algorithms Inference algorithms

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NTIRE 2022 Challenge on Learning the Super-Resolution Space

NTIRE 2022 Challenge on Learning the Super-Resolution Space

引用

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

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Kim, Kang-wook Kim, Younggeun Lee, Jae-young Li, Zechao Pan, Jinshan Shim, Dongseok Song, Ki-Ung Tang, Jinhui Wang, Cong Zhao, Zhihao Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the same low-resolution image, we asked the participants to create methods that sample diverse super-resolution from the space of possible high-resolution images given a low-resolution image. For evaluation, we use the same protocol as introduced in the last year's super-resolution space challenge of NTIRE 2021. We compare the submissions of the participating teams and relate them to the approaches from last year. This challenge contains two tracks: 4x and 8x scale factor. In total, 3 teams competed in the final testing phase.

关键词： computer vision Protocols conferences Superresolution pattern recognition Testing

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Pose Tutor: An Explainable System for Pose Correction in the Wild

Pose Tutor: An Explainable System for Pose Correction in the...

引用

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

作者： Dittakavi, Bhat Bavikadi, Divyagna Desai, Sai Vikas Chakraborty, Soumi Reddy, Nishant Balasubramanian, Vineeth N. Callepalli, Bharathi Sharma, Ayon IIT Hyderabad Hyderabad Telangana India Variance AI Hyderabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Under the new norm of working from home, demand for fitness from home is on the rise. Different exercise forms solve different fitness needs for different people. Yoga gives flexibility and relieves stress. Pilates strengthens the muscles. Kung Fu brings balance. It is not feasible for everyone to hire a personal trainer. In this paper, we develop Pose Tutor, an AI-based explainable pose recognition and correction system. Pose Tutor combines vision and pose skeleton models in a novel coarse-to-fine framework to obtain pose class predictions. An angle-likelihood mechanism is used to explain which human joints maximally caused the pose class predictions and also correct any wrongly formed joints. Even without keypoint level training, Pose Tutor shows promising results on Yoga-82, Pilates-32, and Kungfu-7 datasets. Additionally, user studies conducted with multiple domain experts validate the explanations provided by our framework.

关键词： Training computer vision conferences Predictive models Muscles Skeleton pattern recognition

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Dataset Distillation by Matching Training Trajectories

Dataset Distillation by Matching Training Trajectories

引用

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.

关键词： Training Visualization computer vision conferences Trajectory Task analysis pattern matching

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Dual-Branch Collaborative Transformer for Virtual Try-On

Dual-Branch Collaborative Transformer for Virtual Try-On

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

作者： Fenocchi, Emanuele Morelli, Davide Cornia, Marcella Baraldi, Lorenzo Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET PORTER GRP Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on.

关键词： Convolutional codes Industries Clothing Collaboration computer architecture Transformers pattern recognition

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MinNet: Minutia Patch Embedding Network for Automated Latent Fingerprint recognition

MinNet: Minutia Patch Embedding Network for Automated Latent...

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

作者： Ozturk, Halil Ibrahim Selbes, Berkay Artan, Yusuf HAVELSAN AS Ankara Turkey

ISBN: (纸本)9781665487399

In this study, we proposed a novel minutia patch embedding network (MinNet) model for latent fingerprint recognition task. Embedding vectors generated for a fixed-size patch extracted around a minutia are used in the local similarity assignment algorithm to produce a global similarity match score. Unlike earlier minutia embedding models that aim to discriminate between latent image and sensor image minutia pair embeddings using similar to 2 distance between the embedding vectors in the training process, MinNet model jointly optimizes the spatial and angular distribution of neighboring minutiae and ridge flows of the patches. Even though the proposed model is trained using weakly labeled training data, it produces state-of-the-art results thanks to it ability to generate discriminative embeddings. Proposed method has been evaluated on several public and private datasets and compared to popular latent fingerprint recognition methods presented in earlier studies. Our proposed method significantly outperforms existing methods on all three databases utilized in our study.

关键词： Training Image recognition Databases conferences Emulation Training data Fingerprint recognition

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