5 citations · 14 across the 20 of their papers we have counts for
23 papers
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
Jiahe Wu, Bing Cao, Qilong Wang +3
Multimodal Large Language Models (MLLM) are primarily pre-trained on the RGB modality, thereby limiting their performance on other modalities, such as infrared, depth, and event da…
Reversible Efficient Diffusion for Image Fusion
Xingxin Xu, Bing Cao, DongDong Li +2
Multi-modal image fusion aims to consolidate complementary information from diverse source images into a unified representation. The fused image is expected to preserve fine detail…
Multimodal Negative Learning
Baoquan Gong, Xiyuan Gao, Pengfei Zhu +2
Multimodal learning systems often encounter challenges related to modality imbalance, where a dominant modality may overshadow others, thereby hindering the learning of weak modali…
Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework
Jingjing Wang, Pengyu Liang, Tingyu Wang +12
Solar activity drives space weather, affecting Earth's magnetosphere and technological infrastructure, which makes accurate solar flare forecasting critical. Current space weather…
Generalized Few-Shot Out-of-Distribution Detection
Pinxuan Li, Bing Cao, Changqing Zhang +1
Few-shot Out-of-Distribution (OOD) detection has emerged as a critical research direction in machine learning for practical deployment. Most existing Few-shot OOD detection methods…
Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion
Timing Li, Bing Cao, Jiahe Feng +3
Image fusion synthesizes complementary information from multiple sources, mitigating the inherent limitations of unimodal imaging systems. Accurate image registration is essential…
Bi-directional Self-Registration for Misaligned Infrared-Visible Image Fusion
Timing Li, Bing Cao, Pengfei Zhu +2
Acquiring accurately aligned multi-modal image pairs is fundamental for achieving high-quality multi-modal image fusion. To address the lack of ground truth in current multi-modal…
Dig2DIG: Dig into Diffusion Information Gains for Image Fusion
Bing Cao, Baoshuo Cai, Changqing Zhang +1
Image fusion integrates complementary information from multi-source images to generate more informative results. Recently, the diffusion model, which demonstrates unprecedented gen…
Dream-IF: Dynamic Relative EnhAnceMent for Image Fusion
Xingxin Xu, Bing Cao, Dongdong Li +2
Image fusion aims to integrate comprehensive information from images acquired through multiple sources. However, images captured by diverse sensors often encounter various degradat…
Asymmetric Reinforcing against Multi-modal Representation Bias
Xiyuan Gao, Bing Cao, Pengfei Zhu +2
The strength of multimodal learning lies in its ability to integrate information from various sources, providing rich and comprehensive insights. However, in real-world scenarios,…
: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models
Xiaojie Yin, Qilong Wang, Bing Cao +1
Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between im…
Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion
Yiming Sun, Bing Cao, Pengfei Zhu +1
Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic e…
Test-Time Dynamic Image Fusion
Bing Cao, Yinan Xia, Yi Ding +2
The inherent challenge of image fusion lies in capturing the correlation of multi-source images and comprehensively integrating effective information from different sources. Most e…
Conditional Controllable Image Fusion
Bing Cao, Xingxin Xu, Pengfei Zhu +2
Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually emplo…
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark
Bing Cao, Quanhao Lu, Jiekang Feng +3
The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of target objects. This remains understudied in…
Predictive Dynamic Fusion
Bing Cao, Yinan Xia, Yi Ding +2
Multimodal fusion is crucial in joint decision-making systems for rendering holistic judgments. Since multimodal data changes in open environments, dynamic fusion has emerged and a…
Visible and Clear: Finding Tiny Objects in Difference Map
Bing Cao, Haiyu Yao, Pengfei Zhu +1
Tiny object detection is one of the key challenges in the field of object detection. The performance of most generic detectors dramatically decreases in tiny object detection tasks…
CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
Qinghe Wang, Baolu Li, Xiaomin Li +4
Recent advances in text-to-image models have opened new frontiers in human-centric generation. However, these models cannot be directly employed to generate images with consistent…
Task-Customized Mixture of Adapters for General Image Fusion
Pengfei Zhu, Yang Sun, Bing Cao +1
General image fusion aims at integrating important information from multi-source images. However, due to the significant cross-task gap, the respective fusion mechanism varies cons…
Bi-directional Adapter for Multi-modal Tracking
Bing Cao, Junliang Guo, Pengfei Zhu +1
Due to the rapid development of computer vision, single-modal (RGB) object tracking has made significant progress in recent years. Considering the limitation of single imaging sens…
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Yichen Bai, Zongbo Han, Changqing Zhang +3
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outl…
Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion
Yiming Sun, Bing Cao, Pengfei Zhu +1
Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection,…
HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping
Qinghe Wang, Lijie Liu, Miao Hua +5
Image-based head swapping task aims to stitch a source head to another source body flawlessly. This seldom-studied task faces two major challenges: 1) Preserving the head and body…