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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops"
23095 条 记 录,以下是321-330 订阅
排序:
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
MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving
MV-TAL: Mulit-view Temporal Action Localization in Naturalis...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China
Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in natu... 详细信息
来源: 评论
DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching
DGC-GNN: Leveraging Geometry and Color Cues for Visual Descr...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Shuzhe Kannala, Juho Baratht, Daniel Aalto Univ Dept Comp Sci Espoo Finland Swiss Fed Inst Technol Comp Vision & Geometry Grp Zurich Switzerland
Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements, inherent privacy preservation, and red... 详细信息
来源: 评论
GROUNDHOG : Grounding Large Language Models to Holistic Segmentation
GROUNDHOG : Grounding Large Language Models to Holistic Segm...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Yichi Qiao, Zhiqiao Gao, Xiaofeng Shakiah, Suhaila Gao, Qiaozi Chai, Joyce Univ Michigan Ann Arbor MI 48109 USA Amazon AGI Seattle WA USA
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm la... 详细信息
来源: 评论
Unified-IO 2: Scaling Autoregressive Multimodal Models with vision, Language, Audio, and Action
Unified-IO 2: Scaling Autoregressive Multimodal Models with ...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lu, Jiasen Clark, Christopher Lee, Sangho Zhang, Zichen Khosla, Savya Marten, Ryan Hoiem, Derek Kembhavi, Aniruddha Allen Inst AI Seattle WA 98103 USA Univ Illinois Urbana IL USA Univ Washington Seattle WA 98195 USA
We present UNIFIED-IO 2, the first autoregressive multimodal model that is capable of understanding and generating image, text, audio, and action. To unify different modalities, we tokenize inputs and outputs - images... 详细信息
来源: 评论
HomoFormer: Homogenized Transformer for Image Shadow Removal
HomoFormer: Homogenized Transformer for Image Shadow Removal
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Xiao, Jie Fu, Xueyang Zhu, Yurui Li, Dong Huang, Jie Zhu, Kai Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Hangzhou Peoples R China
The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models, which may lead to an unsatisfactory compromise. To tackle with this issue, we present a... 详细信息
来源: 评论
The Audio-Visual Conversational Graph: From an Egocentric-Exocentric Perspective
The Audio-Visual Conversational Graph: From an Egocentric-Ex...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Jia, Wenqi Liu, Miao Jiang, Hao Ananthabhotla, Ishwarya Rehg, James M. Ithapu, Vamsi Krishna Gao, Ruohan Georgia Tech Atlanta GA 30332 USA Meta Real Labs Menlo Pk CA 94025 USA UIUC Champaign IL USA Meta GenAI Menlo Pk CA USA
In recent years, the thriving development of research related to egocentric videos has provided a unique perspective for the study of conversational interactions, where both visual and audio signals play a crucial rol... 详细信息
来源: 评论
Unified Language-driven Zero-shot Domain Adaptation
Unified Language-driven Zero-shot Domain Adaptation
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yang, Senqiao Tian, Zhuotao Jiang, Li Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China
This paper introduces Unified Language-driven Zero-shot Domain Adaptation ( ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify ... 详细信息
来源: 评论
Grounded Question-Answering in Long Egocentric Videos
Grounded Question-Answering in Long Egocentric Videos
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China
Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-e... 详细信息
来源: 评论