Object Recognition as Classification of Visual Properties
- URL: http://arxiv.org/abs/2112.10531v1
- Date: Mon, 20 Dec 2021 13:50:07 GMT
- Title: Object Recognition as Classification of Visual Properties
- Authors: Fausto Giunchiglia, Mayukh Bagchi
- Abstract summary: We present an object recognition process based on Ranganathan's four-phased faceted knowledge organization process.
We briefly introduce the ongoing project MultiMedia UKC, whose aim is to build an object recognition resource.
- Score: 5.1652563977194434
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We base our work on the teleosemantic modelling of concepts as abilities
implementing the distinct functions of recognition and classification.
Accordingly, we model two types of concepts - substance concepts suited for
object recognition exploiting visual properties, and classification concepts
suited for classification of substance concepts exploiting linguistically
grounded properties. The goal in this paper is to demonstrate that object
recognition can be construed as classification of visual properties, as
distinct from work in mainstream computer vision. Towards that, we present an
object recognition process based on Ranganathan's four-phased faceted knowledge
organization process, grounded in the teleosemantic distinctions of substance
concept and classification concept. We also briefly introduce the ongoing
project MultiMedia UKC, whose aim is to build an object recognition resource
following our proposed process
Related papers
- ECOR: Explainable CLIP for Object Recognition [4.385998292803586]
We propose a mathematical definition of explainability in the object recognition task based on the joint probability distribution of categories and rationales.
Our method demonstrates state-of-the-art performance in explainable classification.
This advancement improves explainable object recognition, enhancing trust across diverse applications.
arXiv Detail & Related papers (2024-04-19T12:20:49Z) - 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) - CLiC: Concept Learning in Context [54.81654147248919]
This paper builds upon recent advancements in visual concept learning.
It involves acquiring a visual concept from a source image and subsequently applying it to an object in a target image.
To localize the concept learning, we employ soft masks that contain both the concept within the mask and the surrounding image area.
arXiv Detail & Related papers (2023-11-28T01:33:18Z) - Intrinsic Physical Concepts Discovery with Object-Centric Predictive
Models [86.25460882547581]
We introduce the PHYsical Concepts Inference NEtwork (PHYCINE), a system that infers physical concepts in different abstract levels without supervision.
We show that object representations containing the discovered physical concepts variables could help achieve better performance in causal reasoning tasks.
arXiv Detail & Related papers (2023-03-03T11:52:21Z) - Building a visual semantics aware object hierarchy [0.0]
We propose a novel unsupervised method to build visual semantics aware object hierarchy.
Our intuition in this paper comes from real-world knowledge representation where concepts are hierarchically organized.
The evaluation consists of two parts, firstly we apply the constructed hierarchy on the object recognition task and then we compare our visual hierarchy and existing lexical hierarchies to show the validity of our method.
arXiv Detail & Related papers (2022-02-26T00:10:21Z) - Visual Ground Truth Construction as Faceted Classification [4.7590051176368915]
Key novelty of our approach lies in the fact that we construct the classification hierarchies from visual properties exploiting visual genus-differentiae.
The proposed approach is validated by a set of experiments on the ImageNet hierarchy of musical experiments.
arXiv Detail & Related papers (2022-02-17T08:35:23Z) - Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs.
We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
arXiv Detail & Related papers (2021-12-21T17:10:21Z) - Translational Concept Embedding for Generalized Compositional Zero-shot
Learning [73.60639796305415]
Generalized compositional zero-shot learning means to learn composed concepts of attribute-object pairs in a zero-shot fashion.
This paper introduces a new approach, termed translational concept embedding, to solve these two difficulties in a unified framework.
arXiv Detail & Related papers (2021-12-20T21:27:51Z) - Classifying concepts via visual properties [5.1652563977194434]
We introduce a general methodology for building lexico-semantic hierarchies of substance concepts.
The key novelty is that the hierarchy is built exploiting the visual properties of substance concepts.
The validity of the approach is exemplified by providing some highlights of an ongoing project.
arXiv Detail & Related papers (2021-05-19T22:24:30Z) - 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) - Look-into-Object: Self-supervised Structure Modeling for Object
Recognition [71.68524003173219]
We propose to "look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions.
We show the recognition backbone can be substantially enhanced for more robust representation learning.
Our approach achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft)
arXiv Detail & Related papers (2020-03-31T12:22:51Z)
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.