咨询与建议

限定检索结果

文献类型

  • 11,885 篇 会议
  • 5 篇 期刊文献

馆藏范围

  • 11,890 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 8,059 篇 工学
    • 7,617 篇 计算机科学与技术...
    • 796 篇 机械工程
    • 688 篇 电气工程
    • 360 篇 软件工程
    • 228 篇 控制科学与工程
    • 40 篇 光学工程
    • 19 篇 生物工程
    • 17 篇 信息与通信工程
    • 12 篇 生物医学工程(可授...
    • 6 篇 电子科学与技术(可...
    • 6 篇 建筑学
    • 6 篇 交通运输工程
    • 5 篇 仪器科学与技术
    • 5 篇 化学工程与技术
    • 5 篇 安全科学与工程
    • 4 篇 土木工程
  • 3,347 篇 医学
    • 3,346 篇 临床医学
    • 4 篇 基础医学(可授医学...
    • 4 篇 公共卫生与预防医...
  • 253 篇 理学
    • 198 篇 系统科学
    • 32 篇 物理学
    • 21 篇 生物学
    • 18 篇 数学
    • 9 篇 统计学(可授理学、...
    • 7 篇 化学
  • 17 篇 管理学
    • 12 篇 管理科学与工程(可...
    • 7 篇 图书情报与档案管...
    • 5 篇 工商管理
  • 3 篇 法学
    • 3 篇 社会学
  • 3 篇 教育学
    • 3 篇 教育学
  • 2 篇 农学
  • 1 篇 经济学
  • 1 篇 军事学

主题

  • 5,633 篇 computer vision
  • 2,668 篇 training
  • 2,203 篇 pattern recognit...
  • 1,747 篇 computational mo...
  • 1,502 篇 visualization
  • 1,360 篇 three-dimensiona...
  • 1,074 篇 semantics
  • 999 篇 benchmark testin...
  • 986 篇 codes
  • 959 篇 computer archite...
  • 891 篇 deep learning
  • 777 篇 conferences
  • 754 篇 task analysis
  • 700 篇 feature extracti...
  • 561 篇 transformers
  • 533 篇 face recognition
  • 527 篇 neural networks
  • 495 篇 object detection
  • 490 篇 image segmentati...
  • 468 篇 cameras

机构

  • 174 篇 univ sci & techn...
  • 145 篇 carnegie mellon ...
  • 144 篇 univ chinese aca...
  • 144 篇 tsinghua univ pe...
  • 134 篇 chinese univ hon...
  • 110 篇 zhejiang univ pe...
  • 109 篇 peng cheng lab p...
  • 99 篇 swiss fed inst t...
  • 91 篇 tsinghua univers...
  • 90 篇 shanghai ai lab ...
  • 87 篇 sensetime res pe...
  • 86 篇 shanghai jiao to...
  • 83 篇 zhejiang univers...
  • 82 篇 tech univ munich...
  • 79 篇 university of sc...
  • 79 篇 stanford univ st...
  • 78 篇 univ hong kong p...
  • 77 篇 australian natl ...
  • 76 篇 alibaba grp peop...
  • 75 篇 peng cheng labor...

作者

  • 75 篇 timofte radu
  • 64 篇 van gool luc
  • 50 篇 zhang lei
  • 43 篇 yang yi
  • 37 篇 loy chen change
  • 36 篇 tao dacheng
  • 32 篇 zhou jie
  • 31 篇 chen chen
  • 30 篇 liu yang
  • 30 篇 tian qi
  • 29 篇 sun jian
  • 29 篇 zha zheng-jun
  • 28 篇 li xin
  • 27 篇 qi tian
  • 26 篇 vasconcelos nuno
  • 25 篇 liu xiaoming
  • 25 篇 darrell trevor
  • 24 篇 zheng wei-shi
  • 24 篇 luo ping
  • 24 篇 ying shan

语言

  • 11,863 篇 英文
  • 26 篇 其他
  • 1 篇 中文
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251-260 订阅
排序:
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual I...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Yu, Qifan Li, Juncheng Wei, Longhui Pang, Liang Ye, Wentao Qin, Bosheng Tang, Siliang Tian, Qi Zhuang, Yueting Zhejiang Univ Hangzhou Peoples R China Huawei Cloud Suzhou Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China
Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks. However, the hallucin... 详细信息
来源: 评论
YOLO-World: Real-Time Open-Vocabulary Object Detection
YOLO-World: Real-Time Open-Vocabulary Object Detection
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Cheng, Tianheng Sone, Lin Ge, Yixiao Liu, Wenyu Wang, Xinggang Shan, Yong Tencent AI Lab Shenzhen Guangdong Peoples R China Tencent PCG ARC Lab Shenzhen Guangdong Peoples R China Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China
The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open sc... 详细信息
来源: 评论
On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?
On the test-time zero-shot generalization of vision-language...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Zanella, Maxime Ben Ayed, Ismail UCLouvain Louvain Belgium UMons Mons Belgium ETS Montreal Montreal PQ Canada
The development of large vision-language models, notably CLIP, has catalyzed research into effective adaptation techniques, with a particular focus on soft prompt tuning. Conjointly, test-time augmentation, which util... 详细信息
来源: 评论
Test-Time Adaptation for Depth Completion
Test-Time Adaptation for Depth Completion
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Park, Hyoungseob Gupta, Anjali Wong, Alex Yale Vision Lab New Haven CT 06501 USA
It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domai... 详细信息
来源: 评论
AnimalFormer: Multimodal vision Framework for Behavior-based Precision Livestock Farming
AnimalFormer: Multimodal Vision Framework for Behavior-based...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Qazi, Ahmed Razzaq, Taha Iqbal, Asim Tibbling Technol Redmond WA 98052 USA
We introduce a multimodal vision framework for precision livestock farming, harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This integrated suite enables comprehensive behavioral analytics from video... 详细信息
来源: 评论
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with vision-Language Models
From Pixels to Graphs: Open-Vocabulary Scene Graph Generatio...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Li, Rongjie Zhang, Songyang Lin, Dahua Chen, Kai He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China
Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for down-stream reasoning tasks. Despite recent advancements, existing methods struggle to generate scene graphs with... 详细信息
来源: 评论
OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and Table recognition
OMNIPARSER: A Unified Framework for Text Spotting, Key Infor...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Wan, Jianqiang Song, Sibo Yu, Wenwen Liu, Yuliang Cheng, Wenqing Huang, Fei Bai, Xiang Yao, Cong Yang, Zhibo Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Hangzhou Peoples R China
Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) c... 详细信息
来源: 评论
Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf vision-Language Models
Emergent Open-Vocabulary Semantic Segmentation from Off-the-...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Luo, Jiayun Khandelwal, Siddhesh Sigal, Leonid Li, Boyang Nanyang Technol Univ Singapore Singapore Univ British Columbia Vector Inst AI Vancouver BC Canada
From image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words, which prove effective for tasks like visual question answering. However, leveraging the learned ... 详细信息
来源: 评论
Distilling vision-Language Models on Millions of Videos
Distilling Vision-Language Models on Millions of Videos
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Zhao, Yue Zhao, Long Zhou, Xingyi Wu, Jialin Chu, Chun-Te Mia, Hui Schroff, Florian Adam, Hartwig Liu, Ting Gong, Boqing Krahenbuhl, Philipp Yuan, Liangzhe Google Res Mountain View CA 94043 USA Univ Texas Austin Austin TX 78712 USA
The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human- curated video-t... 详细信息
来源: 评论
Active Prompt Learning in vision Language Models
Active Prompt Learning in Vision Language Models
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Bang, Jihwan Ahn, Sumyeong Lee, Jae-Gil Korea Adv Inst Sci & Technol Daejeon South Korea Michigan State Univ E Lansing MI USA
Pre-trained vision Language Models (VLMs) have demonstrated notable progress in various zero-shot tasks, such as classification and retrieval. Despite their performance, because improving performance on new tasks requ...
来源: 评论