5 citations · 17 across the 7 of their papers we have counts for
7 papers
Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception
Yuanchen Wu, Lu Zhang, Hang Yao +5
Large Vision-Language Models (LVLMs) have achieved impressive results across various cross-modal tasks. However, hallucinations, i.e., the models generating counterfactual response…
ToVE: Efficient Vision-Language Learning via Knowledge Transfer from Vision Experts
Yuanchen Wu, Junlong Du, Ke Yan +2
Vision-language (VL) learning requires extensive visual perception capabilities, such as fine-grained object recognition and spatial perception. Recent works typically rely on trai…
LaRE: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
Yunpeng Luo, Junlong Du, Ke Yan +1
The evolution of Diffusion Models has dramatically improved image generation quality, making it increasingly difficult to differentiate between real and generated images. This deve…
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding
Yi Xin, Junlong Du, Qiang Wang +2
Large-scale pre-trained models have achieved remarkable success in various computer vision tasks. A standard approach to leverage these models is to fine-tune all model parameters…
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning
Yi Xin, Junlong Du, Qiang Wang +2
Multi-Task Learning (MTL) is designed to train multiple correlated tasks simultaneously, thereby enhancing the performance of individual tasks. Typically, a multi-task network stru…
Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning
Qiang Wang, Junlong Du, Ke Yan +1
The Contrastive Language-Image Pre-training (CLIP) has recently shown remarkable generalization on "zero-shot" training and has applied to many downstream tasks. We explore the ada…