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A note on data-efficient finetuning of vision foundation models

Paper recorded by Signals 4 on 2026-10-27 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28

Category: cs.CV · 计算机视觉 · first seen 2026-10-28

Abstract

Adapting foundation models to a new task within or beyond the training domain is the de facto standard for building specialized machine learning models. Recently, Visual Prompting (VP) has emerged as a promising technique for efficient knowledge transfer. As new foundation model families~(like Mamba) get introduced and VP pipelines such as AutoVP reach greater maturity, we find a growing need for

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#118 most recent of 739 cs.CV papers we have recorded · ↑ newer: ReferStream: Interactive Subject Injection for Streaming Video Generat · ↓ older: ALADDIN: Adversarial Latent Diffusion Distillation for One-Step Identi
Cite this page: A note on data-efficient finetuning of vision foundation models: the #118 most recent of 739 cs.CV papers we have recorded (as of 2026-10-27). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-note-on-data-efficient-finetuning-of-vision-foundation-models.html
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