Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
Parameter-efficient fine-tuning (PEFT) and probing enable adaptation of foundation models using only a small number of trainable parameters, making it 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 acros...
| Authors | Luc P. J. Sträter, Hazel Doughty |
|---|---|
| Primary category | cs.CV |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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