Language-Informed Visual Concept Learning

• URL: http://arxiv.org/abs/2312.03587v2
• Date: Tue, 2 Apr 2024 18:14:35 GMT
• Title: Language-Informed Visual Concept Learning
• Authors: Sharon Lee, Yunzhi Zhang, Shangzhe Wu, Jiajun Wu,
• Abstract summary: We train a set of concept encoders to encode the information pertinent to a set of language-informed concept axes. We then anchor the concept embeddings to a set of text embeddings obtained from a pre-trained Visual Question Answering (VQA) model. At inference time, the model extracts concept embeddings along various axes from new test images, which can be remixed to generate images with novel compositions of visual concepts.
• Score: 22.911347501969857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our understanding of the visual world is centered around various concept axes, characterizing different aspects of visual entities. While different concept axes can be easily specified by language, e.g. color, the exact visual nuances along each axis often exceed the limitations of linguistic articulations, e.g. a particular style of painting. In this work, our goal is to learn a language-informed visual concept representation, by simply distilling large pre-trained vision-language models. Specifically, we train a set of concept encoders to encode the information pertinent to a set of language-informed concept axes, with an objective of reproducing the input image through a pre-trained Text-to-Image (T2I) model. To encourage better disentanglement of different concept encoders, we anchor the concept embeddings to a set of text embeddings obtained from a pre-trained Visual Question Answering (VQA) model. At inference time, the model extracts concept embeddings along various axes from new test images, which can be remixed to generate images with novel compositions of visual concepts. With a lightweight test-time finetuning procedure, it can also generalize to novel concepts unseen at training.

Related papers

• Explainable Concept Generation through Vision-Language Preference Learning [7.736445799116692]
  Concept-based explanations have become a popular choice for explaining deep neural networks post-hoc. We devise a reinforcement learning-based preference optimization algorithm that fine-tunes the vision-language generative model. In addition to showing the efficacy and reliability of our method, we show how our method can be used as a diagnostic tool for analyzing neural networks.
  arXiv  Detail & Related papers  (2024-08-24T02:26:42Z)
• Conceptual Codebook Learning for Vision-Language Models [27.68834532978939]
  We propose Codebook Learning (CoCoLe) to address the challenge of improving the generalization capability of vision-language models (VLMs) We learn a conceptual codebook consisting of visual concepts as keys and conceptual prompts as values. We observe that this conceptual codebook learning method is able to achieve enhanced alignment between visual and linguistic modalities.
  arXiv  Detail & Related papers  (2024-07-02T15:16:06Z)
• Pre-trained Vision-Language Models Learn Discoverable Visual Concepts [33.302556000017844]
  We aim to answer this question as visual concepts learned "for free" would enable wide applications. We assume that the visual concepts, if captured by pre-trained VLMs, can be extracted by their vision-language interface with text-based concept prompts. Our proposed concept discovery and learning framework is thus designed to identify a diverse list of generic visual concepts.
  arXiv  Detail & Related papers  (2024-04-19T06:41:32Z)
• Pix2Code: Learning to Compose Neural Visual Concepts as Programs [23.122886870560247]
  We propose Pix2Code, a framework that extends program synthesis to visual relational reasoning. We show that Pix2Code's representations remain human interpretable and can be easily revised for improved performance.
  arXiv  Detail & Related papers  (2024-02-13T08:14:10Z)
• Text-to-Image Generation for Abstract Concepts [76.32278151607763]
  We propose a framework of Text-to-Image generation for Abstract Concepts (TIAC) The abstract concept is clarified into a clear intent with a detailed definition to avoid ambiguity. The concept-dependent form is retrieved from an LLM-extracted form pattern set.
  arXiv  Detail & Related papers  (2023-09-26T02:22:39Z)
• DeViL: Decoding Vision features into Language [53.88202366696955]
  Post-hoc explanation methods have often been criticised for abstracting away the decision-making process of deep neural networks. In this work, we would like to provide natural language descriptions for what different layers of a vision backbone have learned. We train a transformer network to translate individual image features of any vision layer into a prompt that a separate off-the-shelf language model decodes into natural language.
  arXiv  Detail & Related papers  (2023-09-04T13:59:55Z)
• ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models [79.10890337599166]
  We introduce ConceptBed, a large-scale dataset that consists of 284 unique visual concepts and 33K composite text prompts. We evaluate visual concepts that are either objects, attributes, or styles, and also evaluate four dimensions of compositionality: counting, attributes, relations, and actions. Our results point to a trade-off between learning the concepts and preserving the compositionality which existing approaches struggle to overcome.
  arXiv  Detail & Related papers  (2023-06-07T18:00:38Z)
• Concept Decomposition for Visual Exploration and Inspiration [53.06983340652571]
  We propose a method to decompose a visual concept into different visual aspects encoded in a hierarchical tree structure. We utilize large vision-language models and their rich latent space for concept decomposition and generation.
  arXiv  Detail & Related papers  (2023-05-29T16:56:56Z)
• Designing an Encoder for Fast Personalization of Text-to-Image Models [57.62449900121022]
  We propose an encoder-based domain-tuning approach for text-to-image personalization. We employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain. Second, a set of regularized weight-offsets for the text-to-image model that learn how to effectively ingest additional concepts.
  arXiv  Detail & Related papers  (2023-02-23T18:46:41Z)
• FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations [99.54048050189971]
  We present a framework for learning new visual concepts quickly, guided by multiple naturally occurring data streams. The learned concepts support downstream applications, such as answering questions by reasoning about unseen images. We demonstrate the effectiveness of our model on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-03-30T19:45:00Z)

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.