Knowledge Transfer from Interaction Learning

• URL: http://arxiv.org/abs/2509.18733v1
• Date: Tue, 23 Sep 2025 07:27:36 GMT
• Title: Knowledge Transfer from Interaction Learning
• Authors: Yilin Gao, Kangyi Chen, Zhongxing Peng, Hengjie Lu, Shugong Xu,
• Abstract summary: Current visual foundation models (VFMs) face a fundamental limitation in transferring knowledge from vision language models (VLMs)<n>We propose Learning from Interactions (LFI), a cognitive-inspired framework that explicitly modeling visual understanding as an interactive process.
• Score: 16.83991179271622
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Current visual foundation models (VFMs) face a fundamental limitation in transferring knowledge from vision language models (VLMs), while VLMs excel at modeling cross-modal interactions through unified representation spaces, existing VFMs predominantly adopt result-oriented paradigms that neglect the underlying interaction processes. This representational discrepancy hinders effective knowledge transfer and limits generalization across diverse vision tasks. We propose Learning from Interactions (LFI), a cognitive-inspired framework that addresses this gap by explicitly modeling visual understanding as an interactive process. Our key insight is that capturing the dynamic interaction patterns encoded in pre-trained VLMs enables more faithful and efficient knowledge transfer to VFMs. The approach centers on two technical innovations, Interaction Queries, which maintain persistent relational structures across network layers, and interaction-based supervision, derived from the cross-modal attention mechanisms of VLMs. Comprehensive experiments demonstrate consistent improvements across multiple benchmarks, achieving 3.3 and 1.6mAP/2.4AP absolute gains on TinyImageNet classification and COCO detection/segmentation respectively, with minimal parameter overhead and faster convergence. The framework particularly excels in cross-domain settings, delivering 2.4 and 9.3 zero-shot improvements on PACS and VLCS. Human evaluations further confirm its cognitive alignment, outperforming result-oriented methods by 2.7 times in semantic consistency metrics.

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