Problem
Research questions and friction points this paper is trying to address.
domain experts
image classification
concept encoding
attribute representation
knowledge integration
How to make effective use of domain experts for image classification?
🤖 AI Summary
本文探讨了如何利用领域专家知识改进图像分类，通过微调特征提取器和分类头学习不同格式的属性，并提出了一种自动选择潜在误分类数据的方法以提高分类准确性。
📝 Abstract
A lot of expectations have been put for years on integrating domain expert knowledge in image classification models. Several approaches have been explored, Concept Bottleneck Models (CBMs) opened up a new avenue of research leading to many variants, and more recently to Concept-based Embedding Models (CEMs). CBM consider binary encoding of each concept, while CEM expands this idea by embedding each concept through two vectors. However in real-life scenarii, domain experts' knowledge is usually organized in concepts determined by various attributes, each attribute encoded either with numerical values, or range of values, or binary values, or categorical values. In this work, we first finetune an image feature extractor for classifying attributes representing the downstream object classes, where the class attributes have been specified by experts under various encoding formats. A classification head is then learnt from these various attributes to categorize target objects. We experimentally show that it improves the classification for three datasets: Kaggle fish dataset, AWA2 and a more challenging new wood charcoal dataset. We then propose an automatic selection of potential missclassified data. In this second step, experts are asked for those data to eventually modify the predicted attributes in order to improve the classification.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Concept-based Embedding Models
attribute encoding
expert knowledge integration
automatic selection of misclassified data
🔎 Similar Papers
No similar papers found.
