Clover: Towards A Unified Video-Language Alignment and Fusion Model

• URL: http://arxiv.org/abs/2207.07885v1
• Date: Sat, 16 Jul 2022 09:38:52 GMT
• Title: Clover: Towards A Unified Video-Language Alignment and Fusion Model
• Authors: Jingjia Huang, Yinan Li, Jiashi Feng, Xiaoshuai Sun and Rongrong Ji
• Abstract summary: We introduce Clover, a Correlated Video-Language pre-training method. It improves cross-modal feature alignment and fusion via a novel tri-modal alignment pre-training task. Clover establishes new state-of-the-arts on multiple downstream tasks.
• Score: 154.1070559563592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building a universal video-language model for solving various video understanding tasks (e.g., text-video retrieval, video question answering) is an open challenge to the machine learning field. Towards this goal, most recent attempts train the models, usually consisting of uni-modal and cross-modal feature encoders, with supervised or pair-wise contrastive pre-text tasks. Though offering attractive generality, the resulted models have to compromise between efficiency and performance. We argue the flaws are caused by their pre-training strategies\textemdash they cannot well align and fuse features from different modalities simultaneously. We then introduce Clover -- a Correlated Video-Language pre-training method -- towards a universal video-language model for solving multiple video understanding tasks with neither performance nor efficiency compromise. It improves cross-modal feature alignment and fusion via a novel tri-modal alignment pre-training task. Additionally, we propose to enhance the tri-modal alignment via incorporating learning from masked samples and a novel pair-wise ranking loss. Clover demonstrates outstanding generality. It establishes new state-of-the-arts on multiple downstream tasks, including three retrieval tasks for both zero-shot and fine-tuning settings, and eight video question answering tasks. Codes and pre-trained models will be released at https://github.com/LeeYN-43/Clover.

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