OmniVL:One Foundation Model for Image-Language and Video-Language Tasks

• URL: http://arxiv.org/abs/2209.07526v1
• Date: Thu, 15 Sep 2022 17:59:59 GMT
• Title: OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
• Authors: Junke Wang and Dongdong Chen and Zuxuan Wu and Chong Luo and Luowei Zhou and Yucheng Zhao and Yujia Xie and Ce Liu and Yu-Gang Jiang and Lu Yuan
• Abstract summary: We present OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture. We demonstrate, for the first time, such a paradigm benefits both image and video tasks, as opposed to the conventional one-directional transfer. We introduce a novel unified vision-language contrastive (UniVLC) loss to leverage image-text, video-text, image-label (e.g., image classification), video-label (e.g., video action recognition) data together.
• Score: 117.57580168859512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture. It adopts a unified transformer-based visual encoder for both image and video inputs, and thus can perform joint image-language and video-language pretraining. We demonstrate, for the first time, such a paradigm benefits both image and video tasks, as opposed to the conventional one-directional transfer (e.g., use image-language to help video-language). To this end, we propose a decoupled joint pretraining of image-language and video-language to effectively decompose the vision-language modeling into spatial and temporal dimensions and obtain performance boost on both image and video tasks. Moreover, we introduce a novel unified vision-language contrastive (UniVLC) loss to leverage image-text, video-text, image-label (e.g., image classification), video-label (e.g., video action recognition) data together, so that both supervised and noisily supervised pretraining data are utilized as much as possible. Without incurring extra task-specific adaptors, OmniVL can simultaneously support visual only tasks (e.g., image classification, video action recognition), cross-modal alignment tasks (e.g., image/video-text retrieval), and multi-modal understanding and generation tasks (e.g., image/video question answering, captioning). We evaluate OmniVL on a wide range of downstream tasks and achieve state-of-the-art or competitive results with similar model size and data scale.

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