VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks

• URL: http://arxiv.org/abs/2010.03182v3
• Date: Sun, 25 Oct 2020 05:21:52 GMT
• Title: VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
• Authors: Soyeon Caren Han, Siqu Long, Siwen Luo, Kunze Wang, Josiah Poon
• Abstract summary: We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. We train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation.
• Score: 5.840117063192334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.

Related papers

• LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts [60.54912319612113]
  Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts. We present a novel approach leveraging Large Language Models (LLMs) to extract critical components from text prompts. Our evaluation on complex prompts featuring multiple objects demonstrates a substantial improvement in recall compared to baseline diffusion models.
  arXiv  Detail & Related papers  (2023-10-16T17:57:37Z)
• 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)
• Learning the Visualness of Text Using Large Vision-Language Models [42.75864384249245]
  Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. We curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators.
  arXiv  Detail & Related papers  (2023-05-11T17:45:16Z)
• BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval [61.803481264081036]
  Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text. We tackle this task by a novel underlinetextbfBottom-up crunderlinetextbfOss-modal underlinetextbfSemantic compounderlinetextbfSition (textbfBOSS) with Hybrid Counterfactual Training framework.
  arXiv  Detail & Related papers  (2022-07-09T07:14:44Z)
• Language Matters: A Weakly Supervised Pre-training Approach for Scene Text Detection and Spotting [69.77701325270047]
  This paper presents a weakly supervised pre-training method that can acquire effective scene text representations. Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features. Experiments show that our pre-trained model improves F-score by +2.5% and +4.8% while transferring its weights to other text detection and spotting networks.
  arXiv  Detail & Related papers  (2022-03-08T08:10:45Z)
• 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)
• Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning [50.08729005865331]
  This paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework. To capture the correlations between the image and text at multiple levels of abstraction, we design a variational inference network. To guide the paragraph generation, the learned hierarchical topics and visual features are integrated into the language model.
  arXiv  Detail & Related papers  (2021-05-10T06:55:39Z)
• Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval [8.317191999275536]
  This paper focuses on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval. We employ a Graph Convolutional Network to perform multi-modal reasoning and obtain relationship-enhanced features by learning a common semantic space between salient objects and text found in an image.
  arXiv  Detail & Related papers  (2020-09-21T12:31:42Z)
• TextCaps: a Dataset for Image Captioning with Reading Comprehension [56.89608505010651]
  Text is omnipresent in human environments and frequently critical to understand our surroundings. To study how to comprehend text in the context of an image we collect a novel dataset, TextCaps, with 145k captions for 28k images. Our dataset challenges a model to recognize text, relate it to its visual context, and decide what part of the text to copy or paraphrase.
  arXiv  Detail & Related papers  (2020-03-24T02:38:35Z)
• Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features [8.317191999275536]
  In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities.
  arXiv  Detail & Related papers  (2020-01-14T12:06:12Z)

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.