Automatic Concept Extraction for Concept Bottleneck-based Video Classification

TL;DR

The paper introduces CoDEx, an automatic method for discovering and extracting complex visual concepts from natural language explanations, enabling concept-based video classification without predefined concepts.

Contribution

CoDEx automatically identifies rich, complex concept abstractions from natural language explanations, extending concept bottleneck models to complex video classification tasks.

Findings

CoDEx successfully extracts complex concepts from natural language explanations.

The method enables concept-based classification for complex videos.

Two new datasets demonstrate the approach's effectiveness.

Abstract

Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-level visual concepts from images, e.g., identifying the wing color and beak length for bird-species classification. However, these concept bottleneck models rely on a necessary and sufficient set of predefined concepts-which is intractable for complex tasks such as video classification. For complex tasks, the labels and the relationship between visual elements span many frames, e.g., identifying a bird flying or catching prey-necessitating concepts with various levels of abstraction. To this end, we present CoDEx, an automatic Concept Discovery and Extraction module that rigorously composes a necessary and sufficient set of concept abstractions for concept-based video classification. CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts. To demonstrate our method's viability, we construct two new public datasets that combine existing complex video classification datasets with short, crowd-sourced natural language explanations for their labels. Our method elicits inherent complex concept abstractions in natural language to generalize concept-bottleneck methods to complex tasks.