← 研踪 PaperTrailarXiv:2606.03837 · v2
Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
首次提交 2026-06-03 00:19
版本更新 2026-10-29 20:43
雷达观测 2026-10-30 13:28
摘要
Parameter-efficient finetuning (PEFT) and probing enable adaptation of foundation models using only a small number of trainable parameters. This makes these techniques attractive for video understanding where annotation and computation are expensive. However, video PEFT has focused on adapting image-pretrained models, while standard PEFT methods can also be applied to video representations. These settings are rarely compared and both confine temporal reasoning to a single component of the model, leaving open how temporal context should be distributed across backbone, PEFT, and probe. In this work, we provide a systematic study of model adaptation strategies for video understanding. We evaluate methods across appearance-focused, motion-focused and spatially dense settings, with a particular emphasis on scenarios with limited data where parameter efficiency is most beneficial. Our results provide new insights into PEFT and probing across task types and demonstrate the importance of temporal context allocation for effective video adaptation.
作者备注：NeurIPS 2026 (Evaluations and Datasets Track)
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v2 · 2026-10-29 20:43 · 最新观测版本
Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
Parameter-efficient finetuning (PEFT) and probing enable adaptation of foundation models using only a small number of trainable parameters. This makes these techniques attractive for video understanding where annotation and computation are expensive. However, video PEFT has focused on adapting image-pretrained models, while standard PEFT methods can also be applied to video representations. These settings are rarely compared and both confine temporal reasoning to a single component of the model, leaving open how temporal context should be distributed across backbone, PEFT, and probe. In this work, we provide a systematic study of model adaptation strategies for video understanding. We evaluate methods across appearance-focused, motion-focused and spatially dense settings, with a particular emphasis on scenarios with limited data where parameter efficiency is most beneficial. Our results provide new insights into PEFT and probing across task types and demonstrate the importance of temporal context allocation for effective video adaptation.
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