Prompt Refinement with Image Pivot for Text-to-Image Generation

• URL: http://arxiv.org/abs/2407.00247v1
• Date: Fri, 28 Jun 2024 22:19:24 GMT
• Title: Prompt Refinement with Image Pivot for Text-to-Image Generation
• Authors: Jingtao Zhan, Qingyao Ai, Yiqun Liu, Yingwei Pan, Ting Yao, Jiaxin Mao, Shaoping Ma, Tao Mei,
• Abstract summary: We introduce Prompt Refinement with Image Pivot (PRIP) for text-to-image generation. PRIP decomposes refinement process into two data-rich tasks: inferring representations of user-preferred images from user languages and translating image representations into system languages. It substantially outperforms a wide range of baselines and effectively transfers to unseen systems in a zero-shot manner.
• Score: 103.63292948223592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experience. Such a prompt refinement process is analogous to translating the prompt from "user languages" into "system languages". However, the scarcity of such parallel corpora makes it difficult to train a prompt refinement model. Inspired by zero-shot machine translation techniques, we introduce Prompt Refinement with Image Pivot (PRIP). PRIP innovatively uses the latent representation of a user-preferred image as an intermediary "pivot" between the user and system languages. It decomposes the refinement process into two data-rich tasks: inferring representations of user-preferred images from user languages and subsequently translating image representations into system languages. Thus, it can leverage abundant data for training. Extensive experiments show that PRIP substantially outperforms a wide range of baselines and effectively transfers to unseen systems in a zero-shot manner.

Related papers

• User-Aware Prefix-Tuning is a Good Learner for Personalized Image Captioning [35.211749514733846]
  Traditional image captioning methods often overlook the preferences and characteristics of users. Most existing methods emphasize the user context fusion process by memory networks or transformers. We propose a novel personalized image captioning framework that leverages user context to consider personality factors.
  arXiv  Detail & Related papers  (2023-12-08T02:08:00Z)
• PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation [16.41459454076984]
  This research proposes PromptMagician, a visual analysis system that helps users explore the image results and refine the input prompts. The backbone of our system is a prompt recommendation model that takes user prompts as input, retrieves similar prompt-image pairs from DiffusionDB, and identifies special (important and relevant) prompt keywords.
  arXiv  Detail & Related papers  (2023-07-18T07:46:25Z)
• CoPL: Contextual Prompt Learning for Vision-Language Understanding [21.709017504227823]
  We propose a Contextual Prompt Learning (CoPL) framework, capable of aligning the prompts to the localized features of the image. Our key innovations over earlier works include using local image features as part of the prompt learning process, and more crucially, learning to weight these prompts based on local features that are appropriate for the task at hand. Our method produces substantially improved performance when compared to the current state of the art methods.
  arXiv  Detail & Related papers  (2023-07-03T10:14:33Z)
• Text-based Person Search without Parallel Image-Text Data [52.63433741872629]
  Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description. Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect. In this paper, we make the first attempt to explore TBPS without parallel image-text data.
  arXiv  Detail & Related papers  (2023-05-22T12:13:08Z)
• Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models [29.057923932305123]
  We present Promptify, an interactive system that supports prompt exploration and refinement for text-to-image generative models. Our user study shows that Promptify effectively facilitates the text-to-image workflow and outperforms an existing baseline tool widely used for text-to-image generation.
  arXiv  Detail & Related papers  (2023-04-18T22:59:11Z)
• Texts as Images in Prompt Tuning for Multi-Label Image Recognition [70.9310322461598]
  We advocate that image-text contrastive learning makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-11-23T07:00:11Z)
• Vision-Language Pre-Training for Boosting Scene Text Detectors [57.08046351495244]
  We specifically adapt vision-language joint learning for scene text detection. We propose to learn contextualized, joint representations through vision-language pre-training. The pre-trained model is able to produce more informative representations with richer semantics.
  arXiv  Detail & Related papers  (2022-04-29T03:53:54Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision [57.031588264841]
  We leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme.
  arXiv  Detail & Related papers  (2021-02-11T10:08:12Z)

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.