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

• URL: http://arxiv.org/abs/2504.03970v2
• Date: Thu, 10 Apr 2025 10:41:20 GMT
• Title: VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models
• Authors: Dahun Kim, AJ Piergiovanni, Ganesh Mallya, Anelia Angelova,
• Abstract summary: VideoComp is a benchmark and learning framework for advancing video-text compositionality understanding.<n>We create challenging negative samples with subtle temporal disruptions such as reordering, action word replacement, partial captioning, and combined disruptions.<n>These benchmarks comprehensively test models' compositional sensitivity across extended, cohesive video-text sequences.
• Score: 48.00262713744499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We create challenging negative samples with subtle temporal disruptions such as reordering, action word replacement, partial captioning, and combined disruptions. These benchmarks comprehensively test models' compositional sensitivity across extended, cohesive video-text sequences. To improve model performance, we propose a hierarchical pairwise preference loss that strengthens alignment with temporally accurate pairs and gradually penalizes increasingly disrupted ones, encouraging fine-grained compositional learning. To mitigate the limited availability of densely annotated video data, we introduce a pretraining strategy that concatenates short video-caption pairs to simulate multi-event sequences. We evaluate video-text foundational models and large multimodal models (LMMs) on our benchmark, identifying both strengths and areas for improvement in compositionality. Overall, our work provides a comprehensive framework for evaluating and enhancing model capabilities in achieving fine-grained, temporally coherent video-text alignment.

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