VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models

Understand

We introduce VideoComp, a benchmark and learning framework for advancing video-text compositionality understanding, aimed at improving vision-language models (VLMs) in fine-grained temporal alignment.

• Unlike existing benchmarks focused on static image-text compositionality or isolated single-event videos, our benchmark targets alignment in continuous multi-event videos.
• Leveraging video-text datasets with temporally localized event captions (e.g.
• ActivityNet-Captions, YouCook2), we construct two compositional benchmarks, ActivityNet-Comp and YouCook2-Comp.