文献类型：期刊文献

英文题名：Enhancing Visual In-Context Learning by Multi-Faceted Fusion

作者：Liao, Wenwen[1]; Yu, Jianbo[2]; Wang, Yuansong[3]; Jiang, Qingchao[4]; Yang, Xiaofeng[2]

机构：[1] College of Intelligent Robotics and Advance Manufacturing, Fudan University, China; [2] School of Microelectronics, Fudan University, China; [3] Tsinghua Shenzhen International Graduate School, Tsinghua University, China; [4] School of Information Science and Engineering, East China University of Science and Technology, China

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260092029)

语种：英文

外文关键词：Collaborative learning - Fusion reactions - Image fusion - Object detection

摘要：Visual In-Context Learning (VICL) has emerged as a powerful paradigm, enabling models to perform novel visual tasks by learning from in-context examples. The dominant "retrieve-then-prompt" approach typically relies on selecting the single best visual prompt, a practice that often discards valuable contextual information from other suitable candidates. While recent work has explored fusing the top-K prompts into a single, enhanced representation, this still simply collapses multiple rich signals into one, limiting the model’s reasoning capability. We argue that a more multi-faceted, collaborative fusion is required to unlock the full potential of these diverse contexts. To address this limitation, we introduce a novel framework that moves beyond single-prompt fusion towards an multi-combination collaborative fusion. Instead of collapsing multiple prompts into one, our method generates three contextual representation branches, each formed by integrating information from different combinations of top-quality prompts. These complementary guidance signals are then fed into proposed MULTI-VQGAN architecture, which is designed to jointly interpret and utilize collaborative information from multiple sources. Extensive experiments on diverse tasks, including foreground segmentation, single-object detection, and image colorization, highlight its strong cross-task generalization, effective contextual fusion, and ability to produce more robust and accurate predictions than existing methods. Code will be released after acceptance. Copyright ? 2026, The Authors. All rights reserved.