Getting it Right: Improving Spatial Consistency in Text-to-Image Models

• URL: http://arxiv.org/abs/2404.01197v2
• Date: Tue, 6 Aug 2024 17:58:00 GMT
• Title: Getting it Right: Improving Spatial Consistency in Text-to-Image Models
• Authors: Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, Yezhou Yang,
• Abstract summary: One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. We create SPRIGHT, the first spatially focused, large-scale dataset, by re-captioning 6 million images from 4 widely used vision datasets. We find that training on images containing a larger number of objects leads to substantial improvements in spatial consistency, including state-of-the-art results on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on 500 images.
• Score: 103.52640413616436
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. In this paper, we offer a comprehensive investigation of this limitation, while also developing datasets and methods that support algorithmic solutions to improve spatial reasoning in T2I models. We find that spatial relationships are under-represented in the image descriptions found in current vision-language datasets. To alleviate this data bottleneck, we create SPRIGHT, the first spatially focused, large-scale dataset, by re-captioning 6 million images from 4 widely used vision datasets and through a 3-fold evaluation and analysis pipeline, show that SPRIGHT improves the proportion of spatial relationships in existing datasets. We show the efficacy of SPRIGHT data by showing that using only $\sim$0.25% of SPRIGHT results in a 22% improvement in generating spatially accurate images while also improving FID and CMMD scores. We also find that training on images containing a larger number of objects leads to substantial improvements in spatial consistency, including state-of-the-art results on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on <500 images. Through a set of controlled experiments and ablations, we document additional findings that could support future work that seeks to understand factors that affect spatial consistency in text-to-image models.

Related papers

• REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models [67.55362046790512]
  Vision-language models lack the ability to correctly reason over spatial relationships. We develop the REVISION framework which improves spatial fidelity in vision-language models. Our results and findings indicate that utilizing rendering-based frameworks is an effective approach for developing spatially-aware models.
  arXiv  Detail & Related papers  (2024-08-05T04:51:46Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Spatial Latent Representations in Generative Adversarial Networks for Image Generation [0.0]
  We define a family of spatial latent spaces for StyleGAN2. We show that our spaces are effective for image manipulation and encode semantic information well.
  arXiv  Detail & Related papers  (2023-03-25T20:01:11Z)
• Benchmarking Spatial Relationships in Text-to-Image Generation [102.62422723894232]
  We investigate the ability of text-to-image models to generate correct spatial relationships among objects. We present VISOR, an evaluation metric that captures how accurately the spatial relationship described in text is generated in the image. Our experiments reveal a surprising finding that, although state-of-the-art T2I models exhibit high image quality, they are severely limited in their ability to generate multiple objects or the specified spatial relations between them.
  arXiv  Detail & Related papers  (2022-12-20T06:03:51Z)
• Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval [142.047662926209]
  We propose a novel framework for paired data augmentation by uncovering the hidden semantic information of StyleGAN2 model. We generate augmented text through random token replacement, then pass the augmented text into the latent space alignment module. We evaluate the efficacy of our augmented data approach on two public cross-modal retrieval datasets.
  arXiv  Detail & Related papers  (2022-07-29T01:21:54Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Using Text to Teach Image Retrieval [47.72498265721957]
  We build on the concept of image manifold to represent the feature space of images, learned via neural networks, as a graph. We augment the manifold samples with geometrically aligned text, thereby using a plethora of sentences to teach us about images. The experimental results show that the joint embedding manifold is a robust representation, allowing it to be a better basis to perform image retrieval.
  arXiv  Detail & Related papers  (2020-11-19T16:09:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.