Prompt-Based Multi-Modal Image Segmentation

• URL: http://arxiv.org/abs/2112.10003v1
• Date: Sat, 18 Dec 2021 21:27:19 GMT
• Title: Prompt-Based Multi-Modal Image Segmentation
• Authors: Timo L\"uddecke and Alexander S. Ecker
• Abstract summary: We propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder.
• Score: 81.58378196535003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. Different variants of the latter image-based prompts are analyzed in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties. Source code: https://eckerlab.org/code/clipseg

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