Discovering Conceptual Knowledge with Analytic Ontology Templates for Articulated Objects
- URL: http://arxiv.org/abs/2409.11702v1
- Date: Wed, 18 Sep 2024 04:53:38 GMT
- Title: Discovering Conceptual Knowledge with Analytic Ontology Templates for Articulated Objects
- Authors: Jianhua Sun, Yuxuan Li, Longfei Xu, Jiude Wei, Liang Chai, Cewu Lu,
- Abstract summary: We aim to endow machine intelligence with an analogous capability through performing at the conceptual level.
AOT-driven approach yields benefits in three key perspectives.
- Score: 42.9186628100765
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Human cognition can leverage fundamental conceptual knowledge, like geometric and kinematic ones, to appropriately perceive, comprehend and interact with novel objects. Motivated by this finding, we aim to endow machine intelligence with an analogous capability through performing at the conceptual level, in order to understand and then interact with articulated objects, especially for those in novel categories, which is challenging due to the intricate geometric structures and diverse joint types of articulated objects. To achieve this goal, we propose Analytic Ontology Template (AOT), a parameterized and differentiable program description of generalized conceptual ontologies. A baseline approach called AOTNet driven by AOTs is designed accordingly to equip intelligent agents with these generalized concepts, and then empower the agents to effectively discover the conceptual knowledge on the structure and affordance of articulated objects. The AOT-driven approach yields benefits in three key perspectives: i) enabling concept-level understanding of articulated objects without relying on any real training data, ii) providing analytic structure information, and iii) introducing rich affordance information indicating proper ways of interaction. We conduct exhaustive experiments and the results demonstrate the superiority of our approach in understanding and then interacting with articulated objects.
Related papers
- ConceptFactory: Facilitate 3D Object Knowledge Annotation with Object Conceptualization [41.54457853741178]
ConceptFactory aims at promoting machine intelligence to learn comprehensive object knowledge from both vision and robotics aspects.
It consists of two critical parts: ConceptFactory Suite and ConceptFactory Asset.
arXiv Detail & Related papers (2024-11-01T08:50:04Z) - On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains.
This paper presents a broad review of recent advances in element-wise ZSIR.
We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
arXiv Detail & Related papers (2024-08-09T05:49:21Z) - Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks [24.45212348373868]
This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks.
Our approach appends an unsupervised explanation generator to the primary classifier network and makes use of adversarial training.
This work presents a significant step towards building inherently interpretable deep vision models with task-aligned concept representations.
arXiv Detail & Related papers (2024-01-09T16:16:16Z) - Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction.
We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
arXiv Detail & Related papers (2022-06-03T12:24:49Z) - A Data-Driven Study of Commonsense Knowledge using the ConceptNet
Knowledge Base [8.591839265985412]
Acquiring commonsense knowledge and reasoning is recognized as an important frontier in achieving general Artificial Intelligence (AI)
In this paper, we propose and conduct a systematic study to enable a deeper understanding of commonsense knowledge by doing an empirical and structural analysis of the ConceptNet knowledge base.
Detailed experimental results on three carefully designed research questions, using state-of-the-art unsupervised graph representation learning ('embedding') and clustering techniques, reveal deep substructures in ConceptNet relations.
arXiv Detail & Related papers (2020-11-28T08:08:25Z) - Interpretable Visual Reasoning via Induced Symbolic Space [75.95241948390472]
We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images.
We first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task with object-level visual features.
We then come up with a method to induce concepts of objects and relations using clues from the attention patterns between objects' visual features and question words.
arXiv Detail & Related papers (2020-11-23T18:21:49Z) - Concept Learners for Few-Shot Learning [76.08585517480807]
We propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions.
We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation.
arXiv Detail & Related papers (2020-07-14T22:04:17Z) - Characterizing an Analogical Concept Memory for Architectures
Implementing the Common Model of Cognition [1.468003557277553]
We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories.
We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts.
arXiv Detail & Related papers (2020-06-02T21:54:03Z) - 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.