Grounding from an AI and Cognitive Science Lens

• URL: http://arxiv.org/abs/2402.13290v1
• Date: Mon, 19 Feb 2024 17:44:34 GMT
• Title: Grounding from an AI and Cognitive Science Lens
• Authors: Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth
• Abstract summary: This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities.
• Score: 4.624355582375099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neuro-symbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.

Related papers

• AffordanceLLM: Grounding Affordance from Vision Language Models [36.97072698640563]
  Affordance grounding refers to the task of finding the area of an object with which one can interact. Much of the knowledge is hidden and beyond the image content with the supervised labels from a limited training set. We make an attempt to improve the generalization capability of the current affordance grounding by taking the advantage of the rich world, abstract, and human-object-interaction knowledge.
  arXiv  Detail & Related papers  (2024-01-12T03:21:02Z)
• Grounding for Artificial Intelligence [8.13763396934359]
  grounding is the process of connecting the natural language and abstract knowledge to the internal representation of the real world in an intelligent being. This paper makes an attempt to systematically study this problem.
  arXiv  Detail & Related papers  (2023-12-15T04:45:48Z)
• Grounding Gaps in Language Model Generations [67.79817087930678]
  We study whether large language models generate text that reflects human grounding. We find that -- compared to humans -- LLMs generate language with less conversational grounding. To understand the roots of the identified grounding gap, we examine the role of instruction tuning and preference optimization.
  arXiv  Detail & Related papers  (2023-11-15T17:40:27Z)
• The Vector Grounding Problem [0.0]
  We argue that referential grounding is the one that lies at the heart of the Vector Grounding Problem. We also argue that, perhaps unexpectedly, multimodality and embodiment are neither necessary nor sufficient conditions for referential grounding in artificial systems.
  arXiv  Detail & Related papers  (2023-04-04T02:54:04Z)
• ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding [67.21613160846299]
  Embodied Reference Understanding (ERU) is first designed for this concern. New dataset called ScanERU is constructed to evaluate the effectiveness of this idea.
  arXiv  Detail & Related papers  (2023-03-23T11:36:14Z)
• Grounding 3D Object Affordance from 2D Interactions in Images [128.6316708679246]
  Grounding 3D object affordance seeks to locate objects' ''action possibilities'' regions in the 3D space. Humans possess the ability to perceive object affordances in the physical world through demonstration images or videos. We devise an Interaction-driven 3D Affordance Grounding Network (IAG), which aligns the region feature of objects from different sources.
  arXiv  Detail & Related papers  (2023-03-18T15:37:35Z)
• Grounding 'Grounding' in NLP [59.28887479119075]
  As a community, we use the term broadly to reference any linking of text to data or non-textual modality. Cognitive Science more formally defines "grounding" as the process of establishing what mutual information is required for successful communication.
  arXiv  Detail & Related papers  (2021-06-04T00:40:59Z)
• Learning Zero-Shot Multifaceted Visually Grounded Word Embeddingsvia Multi-Task Training [8.271859911016719]
  Language grounding aims at linking the symbolic representation of language (e.g., words) into the rich perceptual knowledge of the outside world. We argue that this approach sacrifices the abstract knowledge obtained from linguistic co-occurrence statistics in the process of acquiring perceptual information.
  arXiv  Detail & Related papers  (2021-04-15T14:49:11Z)
• Grounded Situation Recognition [56.18102368133022]
  We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images. GSR presents important technical challenges: identifying semantic saliency, categorizing and localizing a large and diverse set of entities. We show initial findings on three exciting future directions enabled by our models: conditional querying, visual chaining, and grounded semantic aware image retrieval.
  arXiv  Detail & Related papers  (2020-03-26T17:57:52Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)

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.