8 papers
State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding
Jiahuan Zhou, Kai Zhu, Zhenyu Cui +3
Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby…
Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation
Jiahuan Zhou, Chao Zhu, Zhenyu Cui +3
Continual Test-Time Adaptation (CTTA) aims to quickly fine-tune the model during the test phase so that it can adapt to multiple unknown downstream domain distributions without pre…
Vision Graph Prompting via Semantic Low-Rank Decomposition
Zixiang Ai, Zichen Liu, Jiahuan Zhou
Vision GNN (ViG) demonstrates superior performance by representing images as graph structures, providing a more natural way to capture irregular semantic patterns beyond traditiona…
Token Coordinated Prompt Attention is Needed for Visual Prompting
Zichen Liu, Xu Zou, Gang Hua +1
Visual prompting techniques are widely used to efficiently fine-tune pretrained Vision Transformers (ViT) by learning a small set of shared prompts for all tokens. However, existin…
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model
Zixiang Ai, Zichen Liu, Yuanhang Lei +3
Pre-trained 3D vision models have gained significant attention for their promising performance on point cloud data. However, fully fine-tuning these models for downstream tasks is…
STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
Zichen Liu, Kunlun Xu, Bing Su +3
Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extend…