Building a visual semantics aware object hierarchy
Xiaolei Diao

TL;DR
This paper introduces an unsupervised approach to construct a visual semantics aware object hierarchy that reduces linguistic bias and improves object recognition by learning from visual features alone.
Contribution
It presents a novel unsupervised method for building a visual semantic hierarchy to enhance classification and address semantic gap issues in computer vision.
Findings
The hierarchy improves object recognition accuracy.
The visual hierarchy outperforms lexical hierarchies.
Preliminary results show the method's efficiency.
Abstract
The semantic gap is defined as the difference between the linguistic representations of the same concept, which usually leads to misunderstanding between individuals with different knowledge backgrounds. Since linguistically annotated images are extensively used for training machine learning models, semantic gap problem (SGP) also results in inevitable bias on image annotations and further leads to poor performance on current computer vision tasks. To address this problem, we propose a novel unsupervised method to build visual semantics aware object hierarchy, aiming to get a classification model by learning from pure-visual information and to dissipate the bias of linguistic representations caused by SGP. Our intuition in this paper comes from real-world knowledge representation where concepts are hierarchically organized, and each concept can be described by a set of features rather…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning
MethodsAttentive Walk-Aggregating Graph Neural Network
