LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation

• URL: http://arxiv.org/abs/2112.13985v1
• Date: Tue, 28 Dec 2021 03:50:03 GMT
• Title: LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation
• Authors: Shoya Matsumori, Yuki Abe, Kosuke Shingyouchi, Komei Sugiura, and Michita Imai
• Abstract summary: We present a novel architecture called a Visually Guided Language Attention GAN (LatteGAN) LatteGAN extracts fine-grained text representations for the generator, and discriminates both the global and local representations of fake or real images. Experiments on two distinct MTIM datasets, CoDraw and i-CLEVR, demonstrate the state-of-the-art performance of the proposed model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Text-guided image manipulation tasks have recently gained attention in the vision-and-language community. While most of the prior studies focused on single-turn manipulation, our goal in this paper is to address the more challenging multi-turn image manipulation (MTIM) task. Previous models for this task successfully generate images iteratively, given a sequence of instructions and a previously generated image. However, this approach suffers from under-generation and a lack of generated quality of the objects that are described in the instructions, which consequently degrades the overall performance. To overcome these problems, we present a novel architecture called a Visually Guided Language Attention GAN (LatteGAN). Here, we address the limitations of the previous approaches by introducing a Visually Guided Language Attention (Latte) module, which extracts fine-grained text representations for the generator, and a Text-Conditioned U-Net discriminator architecture, which discriminates both the global and local representations of fake or real images. Extensive experiments on two distinct MTIM datasets, CoDraw and i-CLEVR, demonstrate the state-of-the-art performance of the proposed model.

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)
• InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists [66.85125112199898]
  We develop a unified language interface for computer vision tasks that abstracts away task-specific design choices. Our model, dubbed InstructCV, performs competitively compared to other generalist and task-specific vision models.
  arXiv  Detail & Related papers  (2023-09-30T14:26:43Z)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)
• IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning [110.7118381246156]
  Increment Reasoning Generative Adversarial Network (IR-GAN) aims to reason consistency between visual increment in images and semantic increment in instructions. First, we introduce the word-level and instruction-level instruction encoders to learn user's intention from history-correlated instructions as semantic increment. Second, we embed the representation of semantic increment into that of source image for generating target image, where source image plays the role of referring auxiliary.
  arXiv  Detail & Related papers  (2022-04-02T07:48:39Z)
• Two-stage Visual Cues Enhancement Network for Referring Image Segmentation [89.49412325699537]
  Referring Image (RIS) aims at segmenting the target object from an image referred by one given natural language expression. In this paper, we tackle this problem by devising a Two-stage Visual cues enhancement Network (TV-Net) Through the two-stage enhancement, our proposed TV-Net enjoys better performances in learning fine-grained matching behaviors between the natural language expression and image.
  arXiv  Detail & Related papers  (2021-10-09T02:53:39Z)
• Text as Neural Operator: Image Manipulation by Text Instruction [68.53181621741632]
  In this paper, we study a setting that allows users to edit an image with multiple objects using complex text instructions to add, remove, or change the objects. The inputs of the task are multimodal including (1) a reference image and (2) an instruction in natural language that describes desired modifications to the image. We show that the proposed model performs favorably against recent strong baselines on three public datasets.
  arXiv  Detail & Related papers  (2020-08-11T07:07:10Z)
• Improving Image Captioning with Better Use of Captions [65.39641077768488]
  We present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences.
  arXiv  Detail & Related papers  (2020-06-21T14:10:47Z)
• XGPT: Cross-modal Generative Pre-Training for Image Captioning [80.26456233277435]
  XGPT is a new method of Cross-modal Generative Pre-Training for Image Captioning. It is designed to pre-train text-to-image caption generators through three novel generation tasks. XGPT can be fine-tuned without any task-specific architecture modifications.
  arXiv  Detail & Related papers  (2020-03-03T12:13:06Z)

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.