2 citations · 7 across the 22 of their papers we have counts for
19 papers · 1 filter
Object-Aware Background-Controlled Editing via Weighted Velocity Guidance
Wuji Wang, Yue Wu, Chenhao Yi +1
Training-free image editing steers diffusion or flow-matching generative models at inference time by modifying prompt-conditioned denoising velocities. Existing velocity-based edit…
DyGT: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer
Yansong Wang, Zhaobo Qi, Xinyan Liu +4
Modeling object dynamics from limited visual observations is a fundamental problem for enabling accurate motion trajectory prediction in embodied interaction scenarios. Existing dy…
ActiveScope: Actively Seeking and Correcting Perception for MLLMs
Yajing Wang, Chao Bi, Junshu Sun +4
Multimodal Large Language Models (MLLMs) have demonstrated impressive vision-language understanding, yet still struggle with fine-grained perception in high-resolution images. Whil…
Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation
Tiantian Dang, Chao Bi, Shufan Shen +3
Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and restricts broader practical deplo…
Enhancing Pre-trained Representation Classifiability can Boost its Interpretability
Shufan Shen, Zhaobo Qi, Junshu Sun +3
The visual representation of a pre-trained model prioritizes the classifiability on downstream tasks, while the widespread applications for pre-trained visual models have posed new…
Kernelized Sparse Fine-Tuning with Bi-level Parameter Competition for Vision Models
Shufan Shen, Junshu Sun, Shuhui Wang +1
Parameter-efficient fine-tuning (PEFT) aims to adapt pre-trained vision models to downstream tasks. Among PEFT paradigms, sparse tuning achieves remarkable performance by adjusting…