机构

• 计算技术研究所 [14]

采集方式

• OAI收割 [14]

内容类型

• 期刊论文 [14]

发表日期

• 2023 [3]
• 2022 [2]
• 2020 [4]
• 2019 [2]
• 2017 [1]
• 2016 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共14条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 Dataset Bias in Few-Shot Image Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 1, 页码: 229-246 作者:  Jiang, Shuqiang;  Zhu, Yaohui;  Liu, Chenlong;  Song, Xinhang;  Li, Xiangyang   |  收藏  |  浏览/下载：6/0  |  提交时间：2023/07/12 Dataset bias  few-shot image recognition  knowledge transfer  meta-learning   Multi-Object Navigation Using Potential Target Position Policy Function 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 2608-2619 作者:  Zeng, Haitao;  Song, Xinhang;  Jiang, Shuqiang   |  收藏  |  浏览/下载：11/0  |  提交时间：2023/12/04 Navigation  Task analysis  Semantics  Visualization  Reinforcement learning  Trajectory  Three-dimensional displays  Multi-object navigation  object navigation  embodied AI   Composite Object Relation Modeling for Few-Shot Scene Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 5678-5691 作者:  Song, Xinhang;  Liu, Chenlong;  Zeng, Haitao;  Zhu, Yaohui;  Chen, Gongwei   |  收藏  |  浏览/下载：3/0  |  提交时间：2023/12/04 Scene recognition  few-shot learning  graph modeling  generalization ability   Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test 期刊论文  OAI收割 FRONTIERS IN ONCOLOGY, 2022, 卷号: 12, 页码: 12 作者:  Ouyang, Hanqiang;  Meng, Fanyu;  Liu, Jianfang;  Song, Xinhang;  Li, Yuan   |  收藏  |  浏览/下载：14/0  |  提交时间：2022/12/07 spine tumor  Turing test  deep learning  MRI  primary tumor   Amorphous Region Context Modeling for Scene Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 141-151 作者:  Zeng, Haitao;  Song, Xinhang;  Chen, Gongwei;  Jiang, Shuqiang   |  收藏  |  浏览/下载：16/0  |  提交时间：2022/12/07 Semantics  Feature extraction  Image segmentation  Convolution  Context modeling  Saliency detection  Layout  Graph neural network  scene recognition  semantic segmentation   Learning Scene Attribute for Scene Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2020, 卷号: 22, 期号: 6, 页码: 1519-1530 作者:  Zeng, Haitao;  Song, Xinhang;  Chen, Gongwei;  Jiang, Shuqiang   |  收藏  |  浏览/下载：20/0  |  提交时间：2020/12/10 Feature extraction  Visualization  Semantics  Context modeling  Image recognition  Computer vision  Aggregates  Scene recognition  scene attribute   Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 525-537 作者:  Song, Xinhang;  Jiang, Shuqiang;  Wang, Bohan;  Chen, Chengpeng;  Chen, Gongwei   |  收藏  |  浏览/下载：28/0  |  提交时间：2020/12/10 Feature extraction  Object detection  Image recognition  Layout  Data models  Recurrent neural networks  Scene recognition  object-to-object relation  sequential representations  RGB-D  object detection   Spatio-Temporal Memory Attention for Image Captioning 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7615-7628 作者:  Ji, Junzhong;  Xu, Cheng;  Zhang, Xiaodan;  Wang, Boyue;  Song, Xinhang   |  收藏  |  浏览/下载：15/0  |  提交时间：2020/12/10 Image captioning  spatio-temporal relationship  attention transmission  memory attention  LSTM   Scene Recognition With Prototype-Agnostic Scene Layout 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 5877-5888 作者:  Chen, Gongwei;  Song, Xinhang;  Zeng, Haitao;  Jiang, Shuqiang   |  收藏  |  浏览/下载：15/0  |  提交时间：2020/12/10 Layout  Semantics  Prototypes  Image recognition  Convolution  Neural networks  Deformable models  Scene classification  convolution neural networks  graph neural networks  scene layout   Learning Effective RGB-D Representations for Scene Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 2, 页码: 980-993 作者:  Jiang, Shuqiang;  Song, Xinhang;  Herranz, Luis;  Chen, Chengpeng   |  收藏  |  浏览/下载：47/0  |  提交时间：2019/04/03 Scene recognition  deep learning  multimodal  RGB-D  video  CNN  RNN