科言猫
学术研究的AI总结
首页
文献互助
订阅
我的收藏
科研工具
选题分析
论文总结
专利管理
未登录
返回
S
Shuohao Li
national university of defense technology
4
H指数
17
论文数
61
被引数
0
相关解读
订阅
收录论文
7
发表时间
发表时间
IF
被引数
Attention-Level Causal Intervention Framework for Multimodal Fake News Detection
多模态虚假新闻检测的注意力层级因果干预框架
Big Data and Cognitive Computing
IF
4.4
2026-09-05
0
OA
AI
Siqi Hao; Shuohao Li; Rongxin Lin; Jun Zhang; Xianghan Wang
分享
收藏
Saliency-Guided Local Semantic Mixing for Long-Tailed Image Classification
saliency-Guided Local Semantic Mixing for Long-Tailed Image Classification
Machine Learning and Knowledge Extraction 2025, Vol. 7, Page 107
IF
6
2025-09-22
0
OA
AI
Jiahui Lv; Jun Lei; Jun Zhang; Chao Chen; Shuohao Li
分享
收藏
Rebalancing in Supervised Contrastive Learning for Long-Tailed Visual Recognition
监督对比学习中用于长尾视觉识别的再平衡方法
big data and cognitive computing
IF
0
2025-08-11
0
OA
AI
Jiahui Lv; Jun Lei; Jun Zhang; Chao Chen; Shuohao Li
分享
收藏
Goal-oriented multi-robot collaborative source search with dynamic exploration–exploitation balance in large-scale constrained areas
目标导向的多机器人协作源搜索,在大型受限区域内实现动态探索-利用平衡
Information Fusion
IF
15.5
2025-07-26
0
OA
AI
Mengyu Yan; Zhengqiu Zhu; Yong Zhao; Bin Chen; Yatai Ji; Kai Xu; Shuohao Li
分享
收藏
Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet
BIG DATA AND COGNITIVE COMPUTING
IF
4.4
2024-09-10
0
OA
AI
Liu, Yang; Li, Xiaofei; Zhang, Jun; Li, Shuohao; Hu, Shengze; Lei, Jun
分享
收藏
FDML: Feature Disentangling and Multi-view Learning for face forgery detection
NEUROCOMPUTING
IF
6.5
2024-03-01
2
PRE
AI
Yu, Miaomiao; Li, Hongying; Yang, Jiaxin; Li, Xiaofei; Li, Shuohao; Zhang, Jun
分享
收藏
Patch-DFD: Patch-based end-to-end DeepFake discriminator
Patch-DFD: 基于补丁的端到端DeepFake鉴别器
NEUROCOMPUTING
IF
6.5
2022-08-01
18
PRE
AI
Yu, Miaomiao; Ju, Sigang; Zhang, Jun; Li, Shuohao; Lei, Jun; Li, Xiaofei
分享
收藏
研究方向
暂时未获取到该数据
合作学者
合作期刊
张
张军
(Jun Zhang)
H 指数: 34 · 论文数: 252
B
Bin Chen
H 指数: 31 · 论文数: 306
H
Hongying Li
H 指数: 29 · 论文数: 168
赵
赵勇
(Yong Zhao)
H 指数: 28 · 论文数: 218
Z
Zhengqiu Zhu
H 指数: 17 · 论文数: 84
查看更多