Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
[Widzenie komputerowe] Autorzy: Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc i 2 więcej
On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves that receives privileged information. Its application to multimodal large language models (MLLMs), however, remains largely unexplored. Recent approaches use privileged visual information, such as image crops corresponding to a question, to improve fine-grained perception, but their gains are confined to tasks that benefit from such visual zooming and require either human-annotated grounding data or external teacher models. We introduce a different form of on-policy self-distillation for MLLMs that provides the teacher with textual, spatially grounded guidance identifying the visual elements relevant to a query. We use procedurally generated scenes with automatically available object identities and spatial coo…
- Kategoria arxiv
- cs.CV
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- Widzenie komputerowe