新研究质疑视觉上下文学习的模型大小

报道来源 [4]

1. arXiv cs.CV TIER_1 English(EN) · Sunil Khatri, Steven Landgraf, Markus Ulrich, Simon Rei{\ss} · 2026-06-10 04:00

   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} · 2026-06-10 04:00

   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 · 2026-06-09 15:08

   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 · 2026-06-09 14:13

   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 …