1.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nanjing UniversityCN486 papers
- University of Science and Technology of ChinaCN408 papers
- Tsinghua UniversityCN404 papers
- Peking UniversityCN401 papers
- Shanghai Jiao Tong UniversityCN383 papers
- Zhejiang UniversityCN380 papers
- University of Chinese Academy of SciencesCN369 papers
- Sun Yat-sen UniversityCN366 papers
- Institute of High Energy PhysicsCN364 papers
- Lanzhou UniversityCN358 papers
- Nanjing Normal UniversityCN358 papers
- Zhengzhou UniversityCN353 papers
13 papers · 2 filters
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation
Yunqi Gu, Tao Zhou, Yizhe Zhang +4
Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of l…
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
Jiaxin Mei, Tao Zhou, Kaiwen Huang +4
Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist…
SpectralGPT: Spectral Remote Sensing Foundation Model
Danfeng Hong, Bing Zhang, Xuyang Li +11
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. Whil…
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
Yuxiang Lai, Yi Zhou, Xinghong Liu +1
Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as t…
A quantum moving target segmentation algorithm for grayscale video
Wenjie Liu, Lu Wang, Qingshan Wu
The moving target segmentation (MTS) aims to segment out moving targets in the video, however, the classical algorithm faces the huge challenge of real-time processing in the curre…
Weakly Supervised Point Clouds Transformer for 3D Object Detection
Zuojin Tang, Bo Sun, Tongwei Ma +2
The annotation of 3D datasets is required for semantic-segmentation and object detection in scene understanding. In this paper we present a framework for the weakly supervision of…
Edge-aware Feature Aggregation Network for Polyp Segmentation
Tao Zhou, Yizhe Zhang, Geng Chen +3
Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundar…
Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training
Chong Liu, Yuqi Zhang, Hongsong Wang +5
Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Mo…
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving
Honghao Fu, Yongli Gu, Yidong Yan +3
With the advancement of vision-based autonomous driving technology, pedestrian detection have become an important component for improving traffic safety and driving system robustne…
MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features
Bo Zhou, Jiapeng Xie, Yan Pan +2
Identifying moving objects is an essential capability for autonomous systems, as it provides critical information for pose estimation, navigation, collision avoidance, and static m…
Transforming Visual Scene Graphs to Image Captions
Xu Yang, Jiawei Peng, Zihua Wang +7
We propose to Transform Scene Graphs (TSG) into more descriptive captions. In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene…
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
Dengyu Wu, Gaojie Jin, Han Yu +2
Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvem…
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Jidong Ge, Yuxiang Liu, Jie Gui +5
Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations…