5 citations · 5 across the 5 of their papers we have counts for
5 papers
Fusion Anything: A Generalized Multimodal Foundation Model
Huizi Cui, Zongbo Han, Chenggong Ding +6
Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, t…
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…
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…
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…
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…