2 citations · 2 across the 10 of their papers we have counts for
11 papers
GAPrompt++: Multi-Granular Geometry-Aware Point Cloud Prompt for 3D Vision Model
Zixiang Ai, Zhenyu Cui, Yufei Guo +4
Pre-trained 3D vision models have substantially advanced point cloud analysis, yet adapting them to downstream tasks via full fine-tuning is computationally expensive and storage-i…
RotVLA: Rotational Latent Action for Vision-Language-Action Model
Qiwei Li, Xicheng Gong, Xinghang Li +5
Latent Action Models (LAMs) have emerged as an effective paradigm for handling heterogeneous datasets during Vision-Language-Action (VLA) model pretraining, offering a unified acti…
RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation
Kai Zhu, Zhenyu Cui, Zehua Zang +1
Recently, state space models have demonstrated efficient video segmentation through linear-complexity state space compression. However, Video Semantic Segmentation (VSS) requires p…
Bi-C2R: Bidirectional Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification
Zhenyu Cui, Jiahuan Zhou, Yuxin Peng
Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its…
CKDA: Cross-modality Knowledge Disentanglement and Alignment for Visible-Infrared Lifelong Person Re-identification
Zhenyu Cui, Jiahuan Zhou, Yuxin Peng
Lifelong person Re-IDentification (LReID) aims to match the same person employing continuously collected individual data from different scenarios. To achieve continuous all-day per…
UPP: Unified Point-Level Prompting for Robust Point Cloud Analysis
Zixiang Ai, Zhenyu Cui, Yuxin Peng +1
Pre-trained point cloud analysis models have shown promising advancements in various downstream tasks, yet their effectiveness is typically suffering from low-quality point cloud (…
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…
SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting
Chenyu Zhang, Kunlun Xu, Zichen Liu +2
Vision-language models (VLMs) encounter considerable challenges when adapting to domain shifts stemming from changes in data distribution. Test-time adaptation (TTA) has emerged as…
DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-Identification
Kunlun Xu, Chenghao Jiang, Peixi Xiong +2
Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Exis…
CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning
Qiwei Li, Jiahuan Zhou
Recently, prompt tuning methods for pre-trained models have demonstrated promising performance in Class Incremental Learning (CIL). These methods typically involve learning task-sp…
Selective Visual Prompting in Vision Mamba
Yifeng Yao, Zichen Liu, Zhenyu Cui +2
Pre-trained Vision Mamba (Vim) models have demonstrated exceptional performance across various computer vision tasks in a computationally efficient manner, attributed to their uniq…