1 citations · 2 across the 13 of their papers we have counts for
8 papers · 1 filter
Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models
Boyang Guo, Liang Li, Lin Peng +3
Prompt learning has emerged as an efficient alternative to fine-tuning pre-trained vision-language models (VLMs). Despite its promise, current methods still struggle to maintain ta…
HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models
Yeqi He, Liang Li, Zhiwen Yang +3
Diffusion models have demonstrated remarkable performance in image generation, particularly within the domain of style transfer. Prevailing style transfer approaches typically leve…
Few-Shot Generative Model Adaption via Identity Injection and Preservation
Yeqi He, Liang Li, Jiehua Zhang +4
Training generative models with limited data presents severe challenges of mode collapse. A common approach is to adapt a large pretrained generative model upon a target domain wit…
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation
Peihua Deng, Jiehua Zhang, Xichun Sheng +4
This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labele…
Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning
Yunbin Tu, Liang Li, Li Su +2
Change captioning aims to succinctly describe the semantic change between a pair of similar images, while being immune to distractors (illumination and viewpoint changes). Under th…
Technique Report of CVPR 2024 PBDL Challenges
Ying Fu, Yu Li, Shaodi You +96
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…
Context-aware Difference Distilling for Multi-change Captioning
Yunbin Tu, Liang Li, Li Su +3
Multi-change captioning aims to describe complex and coupled changes within an image pair in natural language. Compared with single-change captioning, this task requires the model…
Progressive Depth Decoupling and Modulating for Flexible Depth Completion
Zhiwen Yang, Jiehua Zhang, Liang Li +3
Image-guided depth completion aims at generating a dense depth map from sparse LiDAR data and RGB image. Recent methods have shown promising performance by reformulating it as a cl…