Contrastive Video-Language Learning with Fine-grained Frame Sampling

• URL: http://arxiv.org/abs/2210.05039v1
• Date: Mon, 10 Oct 2022 22:48:08 GMT
• Title: Contrastive Video-Language Learning with Fine-grained Frame Sampling
• Authors: Zixu Wang, Yujie Zhong, Yishu Miao, Lin Ma, Lucia Specia
• Abstract summary: FineCo is an approach to better learn video and language representations with a fine-grained contrastive objective operating on video frames. It helps distil a video by selecting the frames that are semantically equivalent to the text, improving cross-modal correspondence.
• Score: 54.542962813921214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck in the area. Most video-language models are trained via pair-level loss to predict whether a pair of video and text is aligned. However, even in paired video-text segments, only a subset of the frames are semantically relevant to the corresponding text, with the remainder representing noise; where the ratio of noisy frames is higher for longer videos. We propose FineCo (Fine-grained Contrastive Loss for Frame Sampling), an approach to better learn video and language representations with a fine-grained contrastive objective operating on video frames. It helps distil a video by selecting the frames that are semantically equivalent to the text, improving cross-modal correspondence. Building on the well established VideoCLIP model as a starting point, FineCo achieves state-of-the-art performance on YouCookII, a text-video retrieval benchmark with long videos. FineCo also achieves competitive results on text-video retrieval (MSR-VTT), and video question answering datasets (MSR-VTT QA and MSR-VTT MC) with shorter videos.

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