Do Vision Models Develop Human-Like Progressive Difficulty Understanding?

Zeyi Huang, Utkarsh Ojha, Yuyang Ji, Yong Jae Lee

Keywords: Understanding Image Classification; Adaptive Testing; Synthetic Benchmarks

Abstract: When a human undertakes a test, their responses likely follow a pattern: if they answered an easy question (2x3) incorrectly, they would likely answer a more difficult one (2x3x4) incorrectly; and if they answered a difficult question correctly, they would likely answer the easy one correctly. Anything else hints at memorization. Do current visual recognition models exhibit a similarly structured learning capacity? In this work, we consider the task of image classification and study if those models' responses follow that pattern. Since real images aren't labeled with difficulty, we first create a dataset of 100 categories, 10 attributes, and 3 difficulty levels using recent generative models: for each category (e.g., dog) and attribute (e.g., occlusion), we generate images of increasing difficulty (e.g., a dog without occlusion, a dog only partly visible). We find that most of the models do in fact behave similarly to the aforementioned pattern around 80-90\% of the time. Using this property, we then present a new way to evaluate those models' image recognition ability. Instead of testing the model on every possible test image, we create an adaptive test akin to GRE, in which the model's performance on the current round of images determines the test images in the next round. This allows the model to skip over questions too easy/hard for itself, and helps us get its overall performance in fewer steps.

Primary Area: datasets and benchmarks

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Submission Number: 186