[Submitted on 29 Sep 2026]
Title:Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs
View a PDF of the paper titled Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs, by Hung-Jen Chen and 6 other authors
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Abstract:Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining distribution, yet fail on specialized domains where the required discriminative features were never learned, a regime we term distant out-of-distribution (OOD). Standard adaptation methods cannot overcome this representational absence because they operate within the encoder's existing feature space. However, VLMs retain a robust descriptive capacity even when discrimination collapses: a model that cannot classify a medical scan can still articulate its visual patterns. Exploiting this asymmetry, we introduce Inductive Visual Logic (IVL), a training-free framework that constructs classification knowledge from the model's surviving descriptive ability. IVL extracts visual traits from few-shot support images through dual-mode prompting, combining semantic descriptions with primitive visual observations, and organizes them into per-class trait dictionaries. At inference, hierarchical filtering identifies spatially grounded trait evidence for classification. Across multiple distant-OOD benchmarks, IVL achieves the highest aggregate accuracy under two VLM backbones while producing interpretable, trait-traceable predictions.
Comments: Accepted by ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.38362 [cs.CV]
(or arXiv:2609.38362v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.38362
arXiv-issued DOI via DataCite
Journal reference: Computer Vision - ECCV 2026, Lecture Notes in Computer Science, vol. 17077, pp. 429-447, Springer, 2026
Related DOI:
https://doi.org/10.1007/978-3-032-37041-9_23
DOI(s) linking to related resources
Submission history
From: Hung-Jen Chen [view email] [v1] Tue, 29 Sep 2026 18:25:04 UTC (19,947 KB)
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