LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation

• URL: http://arxiv.org/abs/2303.12343v2
• Date: Wed, 23 Aug 2023 19:55:51 GMT
• Title: LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation
• Authors: Koutilya Pnvr, Bharat Singh, Pallabi Ghosh, Behjat Siddiquie, David Jacobs
• Abstract summary: We present a technique for segmenting real and AI-generated images using latent diffusion models (LDMs) trained on internet-scale datasets. We show up to 6% improvement over standard baselines for text-to-image segmentation on natural images. For AI-generated imagery, we show close to 20% improvement compared to state-of-the-art techniques.
• Score: 10.623430999818925
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large-scale pre-training tasks like image classification, captioning, or self-supervised techniques do not incentivize learning the semantic boundaries of objects. However, recent generative foundation models built using text-based latent diffusion techniques may learn semantic boundaries. This is because they have to synthesize intricate details about all objects in an image based on a text description. Therefore, we present a technique for segmenting real and AI-generated images using latent diffusion models (LDMs) trained on internet-scale datasets. First, we show that the latent space of LDMs (z-space) is a better input representation compared to other feature representations like RGB images or CLIP encodings for text-based image segmentation. By training the segmentation models on the latent z-space, which creates a compressed representation across several domains like different forms of art, cartoons, illustrations, and photographs, we are also able to bridge the domain gap between real and AI-generated images. We show that the internal features of LDMs contain rich semantic information and present a technique in the form of LD-ZNet to further boost the performance of text-based segmentation. Overall, we show up to 6% improvement over standard baselines for text-to-image segmentation on natural images. For AI-generated imagery, we show close to 20% improvement compared to state-of-the-art techniques. The project is available at https://koutilya-pnvr.github.io/LD-ZNet/.

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