9 citations · 10 across the 4 of their papers we have counts for
5 papers
IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting
Tao Zhang, Yuyang Hong, Yang Xia +5
Recent advances in multimodal large language models (MLLMs) have led to impressive progress across various benchmarks. However, their capability in understanding infrared images re…
UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation
Tao Zhang, Jinyong Wen, Zhen Chen +3
Pre-training techniques significantly enhance the performance of semantic segmentation tasks with limited training data. However, the efficacy under a large domain gap between pre-…
Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level Preference Optimization
Tao Zhang, Cheng Da, Kun Ding +7
Preference optimization for diffusion models aims to align them with human preferences for images. Previous methods typically use Vision-Language Models (VLMs) as pixel-level rewar…
PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images
Tao Zhang, Kun Ding, Jinyong Wen +4
Self-supervised learning (SSL) for RGB images has achieved significant success, yet there is still limited research on SSL for infrared images, primarily due to three prominent cha…
Prompt Tuning with Soft Context Sharing for Vision-Language Models
Kun Ding, Ying Wang, Pengzhang Liu +4
Vision-language models have recently shown great potential on many tasks in computer vision. Meanwhile, prior work demonstrates prompt tuning designed for vision-language models co…