PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding

• URL: http://arxiv.org/abs/2007.00977v1
• Date: Thu, 2 Jul 2020 09:23:08 GMT
• Title: PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding
• Authors: Kanish Garg, Ajeet kumar Singh, Dorien Herremans, Brejesh Lall
• Abstract summary: We propose a method to provide good images by incorporating perceptual understanding in the discriminator module. We show that the perceptual information included in the initial image is improved while modeling image distribution at multiple stages. More importantly, the proposed method can be integrated into the pipeline of other state-of-the-art text-based-image-generation models.
• Score: 11.985768957782641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating an image from a provided descriptive text is quite a challenging task because of the difficulty in incorporating perceptual information (object shapes, colors, and their interactions) along with providing high relevancy related to the provided text. Current methods first generate an initial low-resolution image, which typically has irregular object shapes, colors, and interaction between objects. This initial image is then improved by conditioning on the text. However, these methods mainly address the problem of using text representation efficiently in the refinement of the initially generated image, while the success of this refinement process depends heavily on the quality of the initially generated image, as pointed out in the DM-GAN paper. Hence, we propose a method to provide good initialized images by incorporating perceptual understanding in the discriminator module. We improve the perceptual information at the first stage itself, which results in significant improvement in the final generated image. In this paper, we have applied our approach to the novel StackGAN architecture. We then show that the perceptual information included in the initial image is improved while modeling image distribution at multiple stages. Finally, we generated realistic multi-colored images conditioned by text. These images have good quality along with containing improved basic perceptual information. More importantly, the proposed method can be integrated into the pipeline of other state-of-the-art text-based-image-generation models to generate initial low-resolution images. We also worked on improving the refinement process in StackGAN by augmenting the third stage of the generator-discriminator pair in the StackGAN architecture. Our experimental analysis and comparison with the state-of-the-art on a large but sparse dataset MS COCO further validate the usefulness of our proposed approach.

Related papers

• Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models [20.19571676239579]
  We introduce a novel diffusion-based framework to enhance the alignment of generated images with their corresponding descriptions. Our framework is built upon a comprehensive analysis of inconsistency phenomena, categorizing them based on their manifestation in the image. We then integrate a state-of-the-art controllable image generation model with a visual text generation module to generate an image that is consistent with the original prompt.
  arXiv  Detail & Related papers  (2024-06-24T06:12:16Z)
• CoSeR: Bridging Image and Language for Cognitive Super-Resolution [74.24752388179992]
  We introduce the Cognitive Super-Resolution (CoSeR) framework, empowering SR models with the capacity to comprehend low-resolution images. We achieve this by marrying image appearance and language understanding to generate a cognitive embedding. To further improve image fidelity, we propose a novel condition injection scheme called "All-in-Attention"
  arXiv  Detail & Related papers  (2023-11-27T16:33:29Z)
• Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval [11.798006331912056]
  The goal of Text-to-Image Person Retrieval (TIPR) is to retrieve specific person images according to the given textual descriptions. We propose a novel TIPR framework to build fine-grained interactions and alignment between person images and the corresponding texts.
  arXiv  Detail & Related papers  (2023-07-18T08:23:46Z)
• Improving Scene Text Image Super-resolution via Dual Prior Modulation Network [20.687100711699788]
  Scene text image super-resolution (STISR) aims to simultaneously increase the resolution and legibility of the text images. Existing approaches neglect the global structure of the text, which bounds the semantic determinism of the scene text. Our work proposes a plug-and-play module dubbed Dual Prior Modulation Network (DPMN), which leverages dual image-level priors to bring performance gain over existing approaches.
  arXiv  Detail & Related papers  (2023-02-21T02:59:37Z)
• Modeling Image Composition for Complex Scene Generation [77.10533862854706]
  We present a method that achieves state-of-the-art results on layout-to-image generation tasks. After compressing RGB images into patch tokens, we propose the Transformer with Focal Attention (TwFA) for exploring dependencies of object-to-object, object-to-patch and patch-to-patch.
  arXiv  Detail & Related papers  (2022-06-02T08:34:25Z)
• ViCE: Self-Supervised Visual Concept Embeddings as Contextual and Pixel Appearance Invariant Semantic Representations [77.3590853897664]
  This work presents a self-supervised method to learn dense semantically rich visual embeddings for images inspired by methods for learning word embeddings in NLP.
  arXiv  Detail & Related papers  (2021-11-24T12:27:30Z)
• DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis [55.788772366325105]
  We propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed.
  arXiv  Detail & Related papers  (2021-08-27T07:20:34Z)
• Learned Spatial Representations for Few-shot Talking-Head Synthesis [68.3787368024951]
  We propose a novel approach for few-shot talking-head synthesis. We show that this disentangled representation leads to a significant improvement over previous methods.
  arXiv  Detail & Related papers  (2021-04-29T17:59:42Z)
• RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network [19.017377597937617]
  We study the compositional learning of images and texts for image retrieval. We introduce a novel method that combines the graph convolutional network (GCN) with existing composition methods.
  arXiv  Detail & Related papers  (2021-04-07T09:41:52Z)
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis [80.54273334640285]
  We propose a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators. We also propose a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images.
  arXiv  Detail & Related papers  (2020-08-13T12:51:17Z)

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.