Revisiting the "Video" in Video-Language Understanding

• URL: http://arxiv.org/abs/2206.01720v1
• Date: Fri, 3 Jun 2022 17:57:33 GMT
• Title: Revisiting the "Video" in Video-Language Understanding
• Authors: Shyamal Buch, Crist\'obal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, Juan Carlos Niebles
• Abstract summary: We propose the atemporal probe (ATP), a new model for video-language analysis. We characterize the limitations and potential of current video-language benchmarks. We show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.
• Score: 56.15777956496518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the limitations and potential of current video-language benchmarks. We find that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding. We also demonstrate how ATP can improve both video-language dataset and model design. We describe a technique for leveraging ATP to better disentangle dataset subsets with a higher concentration of temporally challenging data, improving benchmarking efficacy for causal and temporal understanding. Further, we show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.

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