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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"
53104 条 记 录,以下是291-300 订阅
Towards Understanding and Improving Adversarial Robustness of vision Transformers
Towards Understanding and Improving Adversarial Robustness o...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Jain, Samyak Dutta, Tanima Indian Inst Technol BHU Varanasi Varanasi Uttar Pradesh India
Recent literature has demonstrated that vision transformers (VITs) exhibit superior performance compared to convolutional neural networks (CNNs). The majority of recent research on adversarial robustness, however, has... 详细信息
来源: 评论
Multiscale vision Transformers meet Bipartite Matching for efficient single-stage Action Localization
Multiscale Vision Transformers meet Bipartite Matching for e...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ntinou, Ioanna Sanchez, Enrique Tzimiropoulos, Georgios Queen Mary Univ London London England Samsung AI Ctr Cambridge Cambridge England
Action Localization is a challenging problem that combines detection and recognition tasks, which are often addressed separately. State-of-the-art methods rely on off-the-shelf bounding box detections pre-computed at ... 详细信息
来源: 评论
Robust Image Denoising through Adversarial Frequency Mixup
Robust Image Denoising through Adversarial Frequency Mixup
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ryou, Donghun Ha, Inju Yoo, Hyewon Kim, Dongwan Han, Bohyung Seoul Natl Univ ECE Comp Vis Lab Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea
Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks, whi... 详细信息
来源: 评论
ViTamin: Designing Scalable vision Models in the vision-Language Era
ViTamin: Designing Scalable Vision Models in the Vision-Lang...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chen, Jieneng Yu, Qihang Shen, Xiaohui Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA ByteDance Beijing Peoples R China
Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to those from ImageNet-pretrained models, th... 详细信息
来源: 评论
SpatialVLM: Endowing vision-Language Models with Spatial Reasoning Capabilities
SpatialVLM: Endowing Vision-Language Models with Spatial Rea...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chen, Boyuan Xu, Zhuo Kirman, Sean Ichter, Brian Sadigh, Dorsa Guibas, Leonidas Xia, Fei Google DeepMind London England Google Res Mountain View CA USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA
Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While vision Language Models (VLM) have demonstrated remarkable performance in cert... 详细信息
来源: 评论
CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor
CLIP as RNN: Segment Countless Visual Concepts without Train...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Sun, Shuyang Li, Runjia Torr, Philip Gu, Xiuye Li, Siyang Univ Oxford Oxford England Google Res Mountain View CA 94043 USA
Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive, which limits the number of categories in segmentation datasets... 详细信息
来源: 评论
THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large vision-Language Models
THRONE: An Object-based Hallucination Benchmark for the Free...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kaul, Prannay Li, Zhizhong Yang, Hao Dukler, Yonatan Swaminathan, Ashwin Taylor, C. J. Soatto, Stefano Univ Oxford VGG Oxford England AWS AI Labs Oxford England
Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses, which we term "Type I hallucinations&q... 详细信息
来源: 评论
TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process
TIGER: Time-Varying Denoising Model for 3D Point Cloud Gener...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ren, Zhiyuan Kim, Minchul Liu, Feng Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA
Recently, diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However, few works study the effect of the architecture of the diffusion model in the 3D point cloud, re... 详细信息
来源: 评论
DePT: Decoupled Prompt Tuning
DePT: Decoupled Prompt Tuning
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Ji Wu, Shihan Gao, Lianli Shen, Heng Tao Song, Jingkuan Univ Elect Sci & Technol China UESTC Chengdu Peoples R China UESTC Shenzhen Inst Adv Study Chengdu Peoples R China Tongji Univ Shanghai Peoples R China
This work breaks through the Base-New Tradeoff (BNT) dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to new tasks, and vice versa. Specific... 详细信息
来源: 评论
Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing
Attentive Illumination Decomposition Model for Multi-Illumin...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kim, Dongyoung Kim, Jinwoo Yu, Junsang Kim, Seon Joo Yonsei Univ Seoul South Korea Samsung Adv Inst Technol Suwon South Korea
White balance (WB) algorithms in many commercial cameras assume single and uniform illumination, leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior re... 详细信息
来源: 评论