Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?

• URL: http://arxiv.org/abs/2410.13651v1
• Date: Thu, 17 Oct 2024 15:16:10 GMT
• Title: Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
• Authors: Shailaja Keyur Sampat, Maitreya Patel, Yezhou Yang, Chitta Baral,
• Abstract summary: We present a zero-shot framework for fine-grained visual concept learning by leveraging large language model and Visual Question Answering (VQA) system. We pose these questions along with the query image to a VQA system and aggregate the answers to determine the presence or absence of an object in the test images. Our experiments demonstrate comparable performance with existing zero-shot visual classification methods and few-shot concept learning approaches.
• Score: 62.984473889987605
• License:
• Abstract: An ability to learn about new objects from a small amount of visual data and produce convincing linguistic justification about the presence/absence of certain concepts (that collectively compose the object) in novel scenarios is an important characteristic of human cognition. This is possible due to abstraction of attributes/properties that an object is composed of e.g. an object `bird' can be identified by the presence of a beak, feathers, legs, wings, etc. Inspired by this aspect of human reasoning, in this work, we present a zero-shot framework for fine-grained visual concept learning by leveraging large language model and Visual Question Answering (VQA) system. Specifically, we prompt GPT-3 to obtain a rich linguistic description of visual objects in the dataset. We convert the obtained concept descriptions into a set of binary questions. We pose these questions along with the query image to a VQA system and aggregate the answers to determine the presence or absence of an object in the test images. Our experiments demonstrate comparable performance with existing zero-shot visual classification methods and few-shot concept learning approaches, without substantial computational overhead, yet being fully explainable from the reasoning perspective.

Related papers

• 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)
• Explaining Explainability: Understanding Concept Activation Vectors [35.37586279472797]
  Recent interpretability methods propose using concept-based explanations to translate internal representations of deep learning models into a language that humans are familiar with: concepts. This requires understanding which concepts are present in the representation space of a neural network. In this work, we investigate three properties of Concept Activation Vectors (CAVs), which are learnt using a probe dataset of concept exemplars. We introduce tools designed to detect the presence of these properties, provide insight into how they affect the derived explanations, and provide recommendations to minimise their impact.
  arXiv  Detail & Related papers  (2024-04-04T17:46:20Z)
• A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge [39.788346536244504]
  A-OKVQA is a crowdsourced dataset composed of about 25K questions. We demonstrate the potential of this new dataset through a detailed analysis of its contents.
  arXiv  Detail & Related papers  (2022-06-03T17:52:27Z)
• Visual Superordinate Abstraction for Robust Concept Learning [80.15940996821541]
  Concept learning constructs visual representations that are connected to linguistic semantics. We ascribe the bottleneck to a failure of exploring the intrinsic semantic hierarchy of visual concepts. We propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces.
  arXiv  Detail & Related papers  (2022-05-28T14:27:38Z)
• PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning [135.2892665079159]
  We introduce a new large-scale diagnostic visual reasoning dataset named PTR. PTR contains around 70k RGBD synthetic images with ground truth object and part level annotations. We examine several state-of-the-art visual reasoning models on this dataset and observe that they still make many surprising mistakes.
  arXiv  Detail & Related papers  (2021-12-09T18:59:34Z)
• Separating Skills and Concepts for Novel Visual Question Answering [66.46070380927372]
  Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model.
  arXiv  Detail & Related papers  (2021-07-19T18:55:10Z)
• Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning" [49.76230210108583]
  We propose a framework to isolate and evaluate the reasoning aspect of visual question answering (VQA) separately from its perception. We also propose a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception. On the challenging GQA dataset, this framework is used to perform in-depth, disentangled comparisons between well-known VQA models.
  arXiv  Detail & Related papers  (2020-06-20T08:48:29Z)
• CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning [78.3857991931479]
  We present GROLLA, an evaluation framework for Grounded Language Learning with Attributes. We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations.
  arXiv  Detail & Related papers  (2020-06-03T11:21:42Z)

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.