Language-Instructed Vision Embeddings for Controllable and Generalizable Perception
Language-Instructed Vision Embeddings (LIVE) uses language as high-level guidance to produce task-centric embeddings at inference time, removing the need for task-specific retraining. It reduces visual hallucinations by 34 points on MMVP, surpasses much larger vision-language models on VQA, and generalizes to unseen instructions and tasks.
[2606.19584] Language-Instructed Vision Embeddings for Controllable and Generalizable Perception
[Submitted on 17 Jun 2026]
Title:Language-Instructed Vision Embeddings for Controllable and Generalizable Perception
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Abstract:Vision foundation models are typically trained as static feature extractors, placing the burden of task adaptation onto large downstream models. We propose an alternative paradigm: instead of solely feeding visual features into language models, we use language itself to dynamically guide the vision encoder. Our method, Language-Instructed Vision Embeddings (LIVE), leverages language as high-level guidance to produce task-centric embeddings at inference time, removing the need for task-specific retraining. This enables the encoder to focus on contextually relevant aspects of the input, yielding more controllable and generalizable representations. Empirically, LIVE reduces visual hallucinations (+34 points on MMVP), surpasses vision-language models with orders of magnitude more parameters on visual question answering, and generalizes to unseen instructions and tasks -- offering a direct path toward adaptive, instruction-driven visual intelligence.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.19584 [cs.CV]
(or arXiv:2606.19584v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.19584
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Published as a conference paper at ICLR 2026
Submission history
From: Chengzhi Mao [view email] [v1] Wed, 17 Jun 2026 20:39:04 UTC (18,421 KB)
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