General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific models and struggle to maintain robust performance under diverse degradation conditions. In this paper, we propose UniDiffFusion, a unified diffusion framework for multi-task an...
Xing-Xin Xu, Si-Qi Zhao, Xin Li et al.· 0 citations
Supervised infrared-visible image fusion (IVIF) often overfits limited training distributions, creating a critical generalization gap under open-world degradations (rain, haze, low light, noise, blur). To address this issue, we propose AIR-Fusion, a parameter-efficient adaptation of a frozen, restoration-capable latent...
Bing Cao, Qiang Zhang, Xing-Xin Xu et al.· Proceedings of the Thirty-Fi...· 0 citations
This work proposes COllabOrative Knowledge Extraction and integRation (COOKER) method for GDIL to mine inter-domain relationships and uncover the potential of domain-agnostic representations, which significantly outperforms existing baselines.
Jialu Li, Yu Wang, Wanyu Lin et al.· Proceedings of the 32nd ACM...· 0 citations
Multi-modal object detection is essential for robust scene understanding in challenging conditions, including low-light and adverse environments. Recent vision foundation models (e.g., DINOv3) have exhibited strong representation capabilities, yet adapting them to multi-modal scenarios remains challenging. Existing den...
Jiekang Feng, Zhi-He Fan, Yun-Qi Zhu et al.· 0 citations
Graph Domain Incremental Learning (GDIL) aims to acquire knowledge from a continuous stream of graph domains while mitigating catastrophic forgetting. While parameter-isolation methods leveraging graph parameter-efficient adaptation show promise, prompt-based techniques struggle to adapt to GDIL, and low-rank adaptatio...
Jialu Li, Yu Wang, Wanyu Lin et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes Socialized Division and Collaboration as a reformulation of continual learning that decomposes session learning across specialized models in response to optimization conflicts, while enabling coordinated collaboration and introduces an energy-based session-model compatibility criterion grounded in He...
Xinjie Yao, Zhihe Fan, Yunqi Zhu et al.· 0 citations
Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence, makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility.
Xinjie Yao, Xing-Xin Xu, Xi-Yuan Gao et al.· 0 citations
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