Knowledge Transfer from Interaction Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909802178805760 |
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| author | Gao, Yilin Chen, Kangyi Peng, Zhongxing Lu, Hengjie Xu, Shugong |
| author_facet | Gao, Yilin Chen, Kangyi Peng, Zhongxing Lu, Hengjie Xu, Shugong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Transfer from Interaction Learning Gao, Yilin Chen, Kangyi Peng, Zhongxing Lu, Hengjie Xu, Shugong Computer Vision and Pattern Recognition 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. |
| title | Knowledge Transfer from Interaction Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.18733 |