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检索条件"任意字段=2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011"
21179 条 记 录,以下是141-150 订阅
排序:
Improved Visual Grounding through Self-Consistent Explanations
Improved Visual Grounding through Self-Consistent Explanatio...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: He, Ruozhen Cascante-Bonilla, Paola Yang, Ziyan Berg, Alexander C. Ordonez, Vicente Rice Univ Houston TX 77005 USA Univ Calif Irvine Irvine CA USA
vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image. Our work shows that the localization -"groun... 详细信息
来源: 评论
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation
Summarize the Past to Predict the Future: Natural Language D...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Pasca, Razvan-George Gavryushin, Alexey Hamza, Muhammad Kuo, Yen-Ling Mo, Kaichun Van Gool, Luc Hilliges, Otmar Wang, Xi Swiss Fed Inst Technol Zurich Switzerland Univ Zurich Zurich Switzerland Univ Virginia Charlottesville VA USA NVIDIA Santa Clara CA USA Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria
We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects, coined action context. We propose TransFusion, a mul... 详细信息
来源: 评论
Fair-VPT: Fair Visual Prompt Tuning for Image Classification
Fair-VPT: Fair Visual Prompt Tuning for Image Classification
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Park, Sungho Byun, Hy Eran Yonsei Univ Seoul South Korea
Despite the remarkable success of vision Transformers (ViT) across diverse fields in computer vision, they have a clear drawback of expensive adaption cost for downstream tasks due to the increased scale. To address t... 详细信息
来源: 评论
Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration
Dynamic Cues-Assisted Transformer for Robust Point Cloud Reg...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Chen, Hong Yan, Pei Xiang, Sihe Tan, Yihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Hubei Engn Res Ctr Machine Vision & Intelligent S Wuhan Peoples R China
Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have predominantly embraced a coarse-to-fine matching mechanism, with the key to matching the superpoints located in ... 详细信息
来源: 评论
H-ViT: A Hierarchical vision Transformer for Deformable Image Registration
H-ViT: A Hierarchical Vision Transformer for Deformable Imag...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Ghahremani, Morteza Khateri, Mohammad Jian, Bailiang Wiestler, Benedikt Adeli, Ehsan Wachinger, Christian Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany Univ Eastern Finland Espoo Finland Stanford Univ Stanford CA 94305 USA
This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels... 详细信息
来源: 评论
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor ...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Rozenberszki, David Litany, Or Dai, Angela Tech Univ Munich Munich Germany Technion Haifa Israel NVIDIA Santa Clara CA USA
3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive, manual 3D annotations. We propose ... 详细信息
来源: 评论
AM-RADIO: Agglomerative vision Foundation Model Reduce All Domains Into One
AM-RADIO: Agglomerative Vision Foundation Model Reduce All D...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Ranzinger, Mike Heinrich, Greg Kautz, Jan Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA
A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics fo... 详细信息
来源: 评论
PairDETR : Joint Detection and Association of Human Bodies and Faces
PairDETR : Joint Detection and Association of Human Bodies a...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Ali, Ammar Gaikov, Georgii Rybalchenko, Denis Chigorin, Alexander Laptev, Ivan Zagoruyko, Sergey MTS AI ITMO Moscow Russia MTS AI Moscow Russia VisionLabs Hyderabad Telangana India MBZUAI Abu Dhabi U Arab Emirates MTS AI Skoltech Moscow Russia
Image and video analysis requires not only accurate object detection but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object det... 详细信息
来源: 评论
KeyPoint Relative Position Encoding for Face recognition
KeyPoint Relative Position Encoding for Face Recognition
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Kim, Minchul Su, Yiyang Liu, Feng Jain, Anil Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA
In this paper, we address the challenge of making ViT models more robust to unseen affine transformations. Such robustness becomes useful in various recognition tasks such as face recognition when image alignment fail... 详细信息
来源: 评论
Flow-Guided Online Stereo Rectification for Wide Baseline Stereo
Flow-Guided Online Stereo Rectification for Wide Baseline St...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Kumar, Anush Mannan, Fahim Jafari, Omid Hosseini Li, Shile Heider, Felix Torc Robot Blacksburg VA 24060 USA Princeton Univ Princeton NJ 08544 USA
Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However, autonomous vehicles and robots in-the-wild require constant re-calibration... 详细信息
来源: 评论