Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation

• URL: http://arxiv.org/abs/2505.15172v1
• Date: Wed, 21 May 2025 06:42:17 GMT
• Title: Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation
• Authors: Xinran Wang, Muxi Diao, Yuanzhi Liu, Chunyu Wang, Kongming Liang, Zhanyu Ma, Jun Guo,
• Abstract summary: Training text-to-image (T2I) models with detailed captions can significantly improve their generation quality.<n>Existing methods often rely on simplistic metrics like caption length to represent the detailness of the caption in the T2I training set.<n>We propose a new metric to estimate caption detailness based on two aspects: image coverage rate (ICR) and average object detailness (AOD)
• Score: 37.846915788256204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training text-to-image (T2I) models with detailed captions can significantly improve their generation quality. Existing methods often rely on simplistic metrics like caption length to represent the detailness of the caption in the T2I training set. In this paper, we propose a new metric to estimate caption detailness based on two aspects: image coverage rate (ICR), which evaluates whether the caption covers all regions/objects in the image, and average object detailness (AOD), which quantifies the detailness of each object's description. Through experiments on the COCO dataset using ShareGPT4V captions, we demonstrate that T2I models trained on high-ICR and -AOD captions achieve superior performance on DPG and other benchmarks. Notably, our metric enables more effective data selection-training on only 20% of full data surpasses both full-dataset training and length-based selection method, improving alignment and reconstruction ability. These findings highlight the critical role of detail-aware metrics over length-based heuristics in caption selection for T2I tasks.

Related papers

• Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring [26.174094671736686]
  We propose a novel quality-driven data selection pipeline for visual instruction tuning datasets.<n>It integrates a cross-modality assessment framework that first assigns each data entry to its appropriate vision-language task.<n>It generates general and task-specific captions, and evaluates the alignment, clarity, task rarity, text coherence, and image clarity of each entry.
  arXiv  Detail & Related papers  (2025-06-10T04:04:58Z)
• Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching [18.554618623593004]
  Existing methods typically learn a set of embeddings to find the optimal match for each view's text and compute similarity.<n>We propose Dense-to-Sparse Feature Distilled Visual Semantic Embedding (D2S-VSE), which enhances the information capacity of sparse text by leveraging dense text distillation.<n>Our proposed D2S-VSE model is extensively evaluated on the large-scale MS-COCO and Flickr30K datasets.
  arXiv  Detail & Related papers  (2025-03-19T07:42:24Z)
• Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning [56.31096024472269]
  We introduce DeCapBench along with a novel metric, DCScore, specifically designed for detailed captioning tasks.<n>DCScore evaluates hallucinations and fine-grained comprehensiveness by deconstructing responses into the smallest self-sufficient units.<n>DeCapBench exhibits a high correlation with VLM arena results on descriptive tasks, surpassing existing benchmarks for vision-language models.
  arXiv  Detail & Related papers  (2025-03-10T22:53:56Z)
• EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation [29.176750442205325]
  In this study, we contribute an EvalMuse-40K benchmark, gathering 40K image-text pairs with fine-grained human annotations for image-text alignment-related tasks.<n>We introduce two new methods to evaluate the image-text alignment capabilities of T2I models.
  arXiv  Detail & Related papers  (2024-12-24T04:08:25Z)
• A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions [9.87625120950535]
  We collect the Densely Captioned Images dataset, containing 7805 natural images human-annotated with mask-aligned descriptions. With precise and reliable captions associated with specific parts of an image, we can evaluate vision-language models' understanding of image content. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark.
  arXiv  Detail & Related papers  (2023-12-14T00:42:23Z)
• Improving Multimodal Datasets with Image Captioning [65.74736570293622]
  We study how generated captions can increase the utility of web-scraped datapoints with nondescript text. Our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text.
  arXiv  Detail & Related papers  (2023-07-19T17:47:12Z)
• FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions [11.274127953112574]
  We propose an automated approach to augmenting existing captions with visual details using "frozen" vision experts. Our proposed method, FuseCap, fuses the outputs of such vision experts with the original captions using a large language model. We release this large-scale dataset of enriched image-caption pairs for the community.
  arXiv  Detail & Related papers  (2023-05-28T13:16:03Z)
• Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data [95.0476489266988]
  We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Our proposed method trains a captioner to learn from a paired data and to progressively associate unpaired data. Our extensive and comprehensive empirical results both on (1) image-based and (2) dense region-based captioning datasets followed by comprehensive analysis on the scarcely-paired dataset.
  arXiv  Detail & Related papers  (2023-01-26T15:25:43Z)
• Large-Scale Bidirectional Training for Zero-Shot Image Captioning [44.17587735943739]
  We introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.
  arXiv  Detail & Related papers  (2022-11-13T00:09:36Z)
• Paraphrasing Is All You Need for Novel Object Captioning [126.66301869607656]
  Novel object captioning (NOC) aims to describe images containing objects without observing their ground truth captions during training. We present Paraphrasing-to-Captioning (P2C), a two-stage learning framework for NOC, which wouldally optimize the output captions via paraphrasing.
  arXiv  Detail & Related papers  (2022-09-25T22:56:04Z)
• Intrinsic Image Captioning Evaluation [53.51379676690971]
  We propose a learning based metrics for image captioning, which we call Intrinsic Image Captioning Evaluation(I2CE) Experiment results show that our proposed method can keep robust performance and give more flexible scores to candidate captions when encountered with semantic similar expression or less aligned semantics.
  arXiv  Detail & Related papers  (2020-12-14T08:36:05Z)

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.