Concept-Centric Transformers: Concept Transformers with Object-Centric Concept Learning for Interpretability

by   Jinyung Hong, et al.

Attention mechanisms have greatly improved the performance of deep-learning models on visual, NLP, and multimodal tasks while also providing tools to aid in the model's interpretability. In particular, attention scores over input regions or concrete image features can be used to measure how much the attended elements contribute to the model inference. The recently proposed Concept Transformer (CT) generalizes the Transformer attention mechanism from such low-level input features to more abstract, intermediate-level latent concepts that better allow human analysts to more directly assess an explanation for the reasoning of the model about any particular output classification. However, the concept learning employed by CT implicitly assumes that across every image in a class, each image patch makes the same contribution to concepts that characterize membership in that class. Instead of using the CT's image-patch-centric concepts, object-centric concepts could lead to better classification performance as well as better explainability. Thus, we propose Concept-Centric Transformers (CCT), a new family of concept transformers that provides more robust explanations and performance by integrating a novel concept-extraction module based on object-centric learning. We test our proposed CCT against the CT and several other existing approaches on classification problems for MNIST (odd/even), CIFAR100 (super-classes), and CUB-200-2011 (bird species). Our experiments demonstrate that CCT not only achieves significantly better classification accuracy than all selected benchmark classifiers across all three of our test problems, but it generates more consistent concept-based explanations of classification output when compared to CT.


page 6

page 7

page 9

page 18

page 19


XAI for Transformers: Better Explanations through Conservative Propagation

Transformers have become an important workhorse of machine learning, wit...

Patch-based Object-centric Transformers for Efficient Video Generation

In this work, we present Patch-based Object-centric Video Transformer (P...

PatchFormer: A Versatile 3D Transformer Based on Patch Attention

The 3D vision community is witnesses a modeling shift from CNNs to Trans...

RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning

Reasoning about visual relationships is central to how humans interpret ...

Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs

A concept-based classifier can explain the decision process of a deep le...

Evaluating self-attention interpretability through human-grounded experimental protocol

Attention mechanisms have played a crucial role in the development of co...

Fitted Learning: Models with Awareness of their Limits

Though deep learning has pushed the boundaries of classification forward...

Please sign up or login with your details

Forgot password? Click here to reset