Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model

• URL: http://arxiv.org/abs/2411.05079v1
• Date: Thu, 07 Nov 2024 19:00:37 GMT
• Title: Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model
• Authors: Sheng Cheng, Maitreya Patel, Yezhou Yang,
• Abstract summary: We analyze the critical role of caption precision and recall in text-to-image model training. We utilize Large Vision Language Models to generate synthetic captions for training.
• Score: 32.14771853421448
• License:
• Abstract: Despite advancements in text-to-image models, generating images that precisely align with textual descriptions remains challenging due to misalignment in training data. In this paper, we analyze the critical role of caption precision and recall in text-to-image model training. Our analysis of human-annotated captions shows that both precision and recall are important for text-image alignment, but precision has a more significant impact. Leveraging these insights, we utilize Large Vision Language Models to generate synthetic captions for training. Models trained with these synthetic captions show similar behavior to those trained on human-annotated captions, underscores the potential for synthetic data in text-to-image training.

Related papers

• Improving face generation quality and prompt following with synthetic captions [57.47448046728439]
  We introduce a training-free pipeline designed to generate accurate appearance descriptions from images of people. We then use these synthetic captions to fine-tune a text-to-image diffusion model. Our results demonstrate that this approach significantly improves the model's ability to generate high-quality, realistic human faces.
  arXiv  Detail & Related papers  (2024-05-17T15:50:53Z)
• Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning [13.357749288588039]
  Previous works leverage the CLIP's cross-modal association ability for image captioning, relying solely on textual information under unsupervised settings. This paper proposes a novel method to address these issues by incorporating synthetic image-text pairs. A pre-trained text-to-image model is deployed to obtain images that correspond to textual data, and the pseudo features of generated images are optimized toward the real ones in the CLIP embedding space.
  arXiv  Detail & Related papers  (2023-12-14T12:39:29Z)
• Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation [82.5217996570387]
  We adapt a pre-trained language model for auto-regressive text-to-image generation. We find that pre-trained language models offer limited help.
  arXiv  Detail & Related papers  (2023-11-27T07:19:26Z)
• A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation [9.552642210681489]
  We show that by relabeling the corpus with a specialized automatic captioning model and training a text-to-image model on the recaptioned dataset, the model benefits substantially across the board. We analyze various ways to relabel the corpus and provide evidence that this technique, which we call RECAP, both reduces the train-inference discrepancy and provides the model with more information per example.
  arXiv  Detail & Related papers  (2023-10-25T14:10:08Z)
• Dense Text-to-Image Generation with Attention Modulation [49.287458275920514]
  Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions. We propose DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle such dense captions. We achieve similar-quality visual results with models specifically trained with layout conditions.
  arXiv  Detail & Related papers  (2023-08-24T17:59:01Z)
• Image Captions are Natural Prompts for Text-to-Image Models [70.30915140413383]
  We analyze the relationship between the training effect of synthetic data and the synthetic data distribution induced by prompts. We propose a simple yet effective method that prompts text-to-image generative models to synthesize more informative and diverse training data. Our method significantly improves the performance of models trained on synthetic training data.
  arXiv  Detail & Related papers  (2023-07-17T14:38:11Z)
• COSA: Concatenated Sample Pretrained Vision-Language Foundation Model [78.32081709802873]
  Most vision-language foundation models employ image-text datasets for pretraining. We propose COSA, a COncatenated SAmple pretrained vision-language foundation model. We achieve this by sequentially concatenating multiple image-text pairs as inputs for pretraining. This transformation effectively converts existing image-text corpora into a pseudo long-form video-paragraph corpus.
  arXiv  Detail & Related papers  (2023-06-15T12:29:42Z)
• 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)
• LAFITE: Towards Language-Free Training for Text-to-Image Generation [83.2935513540494]
  We propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model. We obtain state-of-the-art results in the standard text-to-image generation tasks.
  arXiv  Detail & Related papers  (2021-11-27T01:54:45Z)

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.