Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions

• URL: http://arxiv.org/abs/2111.10337v1
• Date: Fri, 19 Nov 2021 17:36:01 GMT
• Title: Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions
• Authors: Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, Baining Guo
• Abstract summary: We study joint and language (VL) pre-training to enable cross-modality learning and benefit plentiful downstream tasks. Our model achieves new state-of-the-art results in 10 understanding tasks and 2 more novel text-to-visual generation tasks.
• Score: 31.4943447481144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study joint video and language (VL) pre-training to enable cross-modality learning and benefit plentiful downstream VL tasks. Existing works either extract low-quality video features or learn limited text embedding, while neglecting that high-resolution videos and diversified semantics can significantly improve cross-modality learning. In this paper, we propose a novel High-resolution and Diversified VIdeo-LAnguage pre-training model (HD-VILA) for many visual tasks. In particular, we collect a large dataset with two distinct properties: 1) the first high-resolution dataset including 371.5k hours of 720p videos, and 2) the most diversified dataset covering 15 popular YouTube categories. To enable VL pre-training, we jointly optimize the HD-VILA model by a hybrid Transformer that learns rich spatiotemporal features, and a multimodal Transformer that enforces interactions of the learned video features with diversified texts. Our pre-training model achieves new state-of-the-art results in 10 VL understanding tasks and 2 more novel text-to-visual generation tasks. For example, we outperform SOTA models with relative increases of 38.5% R@1 in zero-shot MSR-VTT text-to-video retrieval task, and 53.6% in high-resolution dataset LSMDC. The learned VL embedding is also effective in generating visually pleasing and semantically relevant results in text-to-visual manipulation and super-resolution tasks.

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