  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.
