71 citations · 76 across the 8 of their papers we have counts for
7 papers
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
Yuwei Tang, Zhenyi Lin, Qilong Wang +2
Recently, pre-trained vision-language models (e.g., CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made…
Task-Customized Mixture of Adapters for General Image Fusion
Pengfei Zhu, Yang Sun, Bing Cao +1
General image fusion aims at integrating important information from multi-source images. However, due to the significant cross-task gap, the respective fusion mechanism varies cons…
Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
Pengfei Zhu, Qian Wang, Yu Wang +2
Attributed graph clustering is an unsupervised task that partitions nodes into different groups. Self-supervised learning (SSL) shows great potential in handling this task, and som…
Exploring Diverse Representations for Open Set Recognition
Yu Wang, Junxian Mu, Pengfei Zhu +1
Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform…
Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction
Yuxin Yang, Pengfei Zhu, Mengshi Qi +1
Human trajectory forecasting is a critical challenge in fields such as robotics and autonomous driving. Due to the inherent uncertainty of human actions and intentions in real-worl…
Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion
Yiming Sun, Bing Cao, Pengfei Zhu +1
Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection,…