92 citations · 213 across the 12 of their papers we have counts for
22 papers · 1 filter
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…
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…
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
Kun Ding, Haojian Zhang, Qiang Yu +3
We propose a generalized method for boosting the generalization ability of pre-trained vision-language models (VLMs) while fine-tuning on downstream few-shot tasks. The idea is rea…
Reusable Architecture Growth for Continual Stereo Matching
Chenghao Zhang, Gaofeng Meng, Bin Fan +4
The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks…
Compositional Kronecker Context Optimization for Vision-Language Models
Kun Ding, Xiaohui Li, Qiang Yu +3
Context Optimization (CoOp) has emerged as a simple yet effective technique for adapting CLIP-like vision-language models to downstream image recognition tasks. Nevertheless, learn…
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…