Evaluating the Generation of Spatial Relations in Text and Image Generative Models

• URL: http://arxiv.org/abs/2411.07664v1
• Date: Tue, 12 Nov 2024 09:30:02 GMT
• Title: Evaluating the Generation of Spatial Relations in Text and Image Generative Models
• Authors: Shang Hong Sim, Clarence Lee, Alvin Tan, Cheston Tan,
• Abstract summary: spatial relations are naturally understood in a visuo-spatial manner. We develop an approach to convert LLM outputs into an image, thereby allowing us to evaluate both T2I models and LLMs. Surprisingly, we found that T2I models only achieve subpar performance despite their impressive general image-generation abilities.
• Score: 4.281091463408283
• License:
• Abstract: Understanding spatial relations is a crucial cognitive ability for both humans and AI. While current research has predominantly focused on the benchmarking of text-to-image (T2I) models, we propose a more comprehensive evaluation that includes \textit{both} T2I and Large Language Models (LLMs). As spatial relations are naturally understood in a visuo-spatial manner, we develop an approach to convert LLM outputs into an image, thereby allowing us to evaluate both T2I models and LLMs \textit{visually}. We examined the spatial relation understanding of 8 prominent generative models (3 T2I models and 5 LLMs) on a set of 10 common prepositions, as well as assess the feasibility of automatic evaluation methods. Surprisingly, we found that T2I models only achieve subpar performance despite their impressive general image-generation abilities. Even more surprisingly, our results show that LLMs are significantly more accurate than T2I models in generating spatial relations, despite being primarily trained on textual data. We examined reasons for model failures and highlight gaps that can be filled to enable more spatially faithful generations.

Related papers

• FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing [32.01426831450348]
  We introduce FairT2I, a novel framework that harnesses large language models to detect and mitigate social biases in T2I generation. Our results show that FairT2I successfully mitigates social biases and enhances the diversity of sensitive attributes in generated images.
  arXiv  Detail & Related papers  (2025-02-06T07:22:57Z)
• Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models [54.052963634384945]
  We introduce the Image Regeneration task to assess text-to-image models. We use GPT4V to bridge the gap between the reference image and the text input for the T2I model. We also present ImageRepainter framework to enhance the quality of generated images.
  arXiv  Detail & Related papers  (2024-11-14T13:52:43Z)
• SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data [73.23388142296535]
  SELMA improves the faithfulness of T2I models by fine-tuning models on automatically generated, multi-skill image-text datasets. We show that SELMA significantly improves the semantic alignment and text faithfulness of state-of-the-art T2I diffusion models on multiple benchmarks. We also show that fine-tuning with image-text pairs auto-collected via SELMA shows comparable performance to fine-tuning with ground truth data.
  arXiv  Detail & Related papers  (2024-03-11T17:35:33Z)
• The All-Seeing Project V2: Towards General Relation Comprehension of the Open World [58.40101895719467]
  We present the All-Seeing Project V2, a new model and dataset designed for understanding object relations in images. We propose the All-Seeing Model V2 that integrates the formulation of text generation, object localization, and relation comprehension into a relation conversation task. Our model excels not only in perceiving and recognizing all objects within the image but also in grasping the intricate relation graph between them.
  arXiv  Detail & Related papers  (2024-02-29T18:59:17Z)
• Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models [71.49054220807983]
  A prevalent limitation persists in the effective communication with T2I models, such as Stable Diffusion, using natural language descriptions. Inspired by the recently released DALLE3, we revisit the existing T2I systems endeavoring to align human intent and introduce a new task - interactive text to image (iT2I) We present a simple approach that augments LLMs for iT2I with prompting techniques and off-the-shelf T2I models.
  arXiv  Detail & Related papers  (2023-10-11T16:53:40Z)
• 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)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)

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.