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New research questions model size for visual in-context learning

By PulseAugur Editorial · [4 sources] ·

Two new research papers published on arXiv explore the effectiveness of visual in-context learning (VICL). One paper challenges the notion that large models and extensive data are essential for VICL by training a tiny model with only 1 million parameters and 70,000 images. The other paper introduces VIBE, a comprehensive benchmark designed to evaluate VICL models across diverse domains and tasks, highlighting limitations in current adaptation capability assessments. AI

IMPACT Highlights potential for smaller models in visual adaptation and calls for improved benchmarking in the field.

RANK_REASON Two research papers published on arXiv discussing visual in-context learning.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

COVERAGE [4]

  1. arXiv cs.CV TIER_1 English(EN) · Sunil Khatri, Steven Landgraf, Markus Ulrich, Simon Rei{\ss} ·

    Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model

    arXiv:2606.10905v1 Announce Type: new Abstract: Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at test-time. With the history of in-context learning in natural language processin…

  2. arXiv cs.CV TIER_1 English(EN) · Pradnya Halady, Jiale Wei, Zdravko Marinov, Alexander Jaus, Simon Rei{\ss} ·

    Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks

    arXiv:2606.10967v1 Announce Type: new Abstract: Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby can adapt to new vision tasks at test-time. Yet, the evaluation of the adaptation…

  3. arXiv cs.CV TIER_1 English(EN) · Simon Reiß ·

    Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks

    Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby can adapt to new vision tasks at test-time. Yet, the evaluation of the adaptation capabilities of these models has been limited t…

  4. arXiv cs.CV TIER_1 English(EN) · Simon Reiß ·

    Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model

    Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at test-time. With the history of in-context learning in natural language processing research, where large, parameter-heavy models …

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