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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"
11890 条 记 录,以下是451-460 订阅
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Visual Navigation with Spatial Attention
Visual Navigation with Spatial Attention
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Mayo, Bar Hazan, Tamir Tal, Ayellet Technion Haifa Israel
This work focuses on object goal visual navigation, aiming at finding the location of an object from a given class, where in each step the agent is provided with an egocentric RGB image of the scene. We propose to lea... 详细信息
来源: 评论
Towards Explainable Visual Vessel recognition Using Fine-Grained Classification and Image Retrieval
Towards Explainable Visual Vessel Recognition Using Fine-Gra...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Karus, Heiko Schwenker, Friedhelm Munz, Michael Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Ulm Univ Ulm Germany Ulm Univ Appl Sci Ulm Germany
The precise recognition of vessel types is critical for applications in maritime surveillance, but manual visual inspection is slow and error-prone. Automated fine-grained object recognition helps to quickly and accur... 详细信息
来源: 评论
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of t...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Wang, Hongjie Dedhia, Bhishma Jha, Niraj K. Princeton Univ Princeton NJ 08540 USA
Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pr... 详细信息
来源: 评论
Pre-training vision Models with Mandelbulb Variations
Pre-training Vision Models with Mandelbulb Variations
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Chiche, Benjamin Naoto Horikawa, Yuto Fujita, Ryo Rist Inc 830 Hongakujimae-ChoGojo-DoriShimogyo-Ku Kyoto 6008102 Japan
The use of models that have been pre-trained on natural image datasets like ImageNet may face some limitations. First, this use may be restricted due to copyright and license on the training images, and privacy laws. ... 详细信息
来源: 评论
Neural Contours: Learning to Draw Lines from 3D Shapes
Neural Contours: Learning to Draw Lines from 3D Shapes
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Liu, Difan Nabail, Mohamed Hertzmann, Aaron Kalogerakis, Evangelos Univ Massachusetts Amherst Amherst MA 01003 USA Adobe Res San Francisco CA USA
This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3D model, and an image-based module op... 详细信息
来源: 评论
NICE: cvpr 2023 Challenge on Zero-shot Image Captioning
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Kim, Taehoon Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Marsden, Mark Sala, Alessandra Kim, Seung Hwan Han, Bohyung Lee, Kyoung Mu Lee, Honglak Bae, Kyounghoon Wu, Xiangyu Gao, Yi Zhang, Hailiang Yang, Yang Guo, Weili Lu, Jianfeng Oh, Youngtaek Cho, Jae Won Kim, Dong-Jin Kweon, In So Kim, Junmo Kang, Wooyoung Jhoo, Won Young Roh, Byungseok Mun, Jonghwan Oh, Solgil Ak, Kenan Emir Lee, Gwang-Gook Xu, Yan Shen, Mingwei Hwang, Kyomin Shin, Wonsik Lee, Kamin Park, Wonhark Lee, Dongkwan Kwak, Nojun Wang, Yujin Wang, Yimu Gu, Tiancheng Lv, Xingchang Sun, Mingmao
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision comm... 详细信息
来源: 评论
Color Shift Estimation-and-Correction for Image Enhancement
Color Shift Estimation-and-Correction for Image Enhancement
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Li, Yiyu Xu, Ke Hancke, Gerhard Petrus Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China
Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate color tone distortion in undere... 详细信息
来源: 评论
AHIVE: Anatomy-aware Hierarchical vision Encoding for Interactive Radiology Report Retrieval
AHIVE: Anatomy-aware Hierarchical Vision Encoding for Intera...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Yan, Sixing Cheung, William K. Tsang, Ivor W. Chiu, Keith Tong, Terence M. Cheung, Ka Chun Seel, Simon Hong Kong Baptist Univ Hong Kong Peoples R China Agcy Sci Technol & Res CFAR Singapore Singapore Agcy Sci Technol & Res IHPC Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Univ Technol Sydney AAII Sydney NSW Australia Queen Elizabeth Hosp Hong Kong Peoples R China Kwong Wah Hosp Hong Kong Peoples R China Tuen Mun Hosp Hong Kong Peoples R China NVIDIA Corp NVIDIA AI Technol Ctr Santa Clara CA USA
Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation, where irrelevant and unfaithful contents ar... 详细信息
来源: 评论
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for vision Applications
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Xiong, Yuwen Li, Zhiqi Chen, Yuntao Wang, Feng Zhu, Xizhou Luo, Jiapeng Wang, Wenhai Lu, Tong Li, Hongsheng Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Univ Toronto Toronto ON Canada Shanghai AI Lab OpenGVLab Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Chinese Acad Sci CAIR HKISI Beijing Peoples R China Tsinghua Univ Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China
We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor, DCNv3, with two key e...
来源: 评论
TransNeXt: Robust Foveal Visual Perception for vision Transformers
TransNeXt: Robust Foveal Visual Perception for Vision Transf...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Shi, Dai
Due to the depth degradation effect in residual connections, many efficient vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing, leading to... 详细信息
来源: 评论