IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks

• URL: http://arxiv.org/abs/2312.01771v1
• Date: Mon, 4 Dec 2023 09:48:29 GMT
• Title: IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks
• Authors: Jiarui Xu, Yossi Gandelsman, Amir Bar, Jianwei Yang, Jianfeng Gao, Trevor Darrell, Xiaolong Wang
• Abstract summary: In this paper, we present IMProv, a generative model that is able to in-context learn visual tasks from multimodal prompts. We train a masked generative transformer on a new dataset of figures from computer vision papers and their associated captions. During inference time, we prompt the model with text and/or image task example(s) and have the model inpaint the corresponding output.
• Score: 124.90137528319273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context learning allows adapting a model to new tasks given a task description at test time. In this paper, we present IMProv - a generative model that is able to in-context learn visual tasks from multimodal prompts. Given a textual description of a visual task (e.g. "Left: input image, Right: foreground segmentation"), a few input-output visual examples, or both, the model in-context learns to solve it for a new test input. We train a masked generative transformer on a new dataset of figures from computer vision papers and their associated captions, together with a captioned large-scale image-text dataset. During inference time, we prompt the model with text and/or image task example(s) and have the model inpaint the corresponding output. We show that training our model with text conditioning and scaling the dataset size improves in-context learning for computer vision tasks by over +10\% AP for Foreground Segmentation, over +5\% gains in AP for Single Object Detection, and almost 20\% lower LPIPS in Colorization. Our empirical results suggest that vision and language prompts are complementary and it is advantageous to use both to achieve better in-context learning performance. Project page is available at https://jerryxu.net/IMProv .

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