SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks
This addresses the issue of robustness in computer vision for real-world applications, though it is incremental as it builds on existing research on image perturbations.
The paper tackles the problem of deep learning models' sensitivity to image capture conditions by introducing the SNAP benchmark, which reveals significant biases in vision datasets and shows that models fail to reach human accuracy even on well-exposed images, with performance drops of up to 30% under varying camera settings.
Generalization of deep-learning-based (DL) computer vision algorithms to various image perturbations is hard to establish and remains an active area of research. The majority of past analyses focused on the images already captured, whereas effects of the image formation pipeline and environment are less studied. In this paper, we address this issue by analyzing the impact of capture conditions, such as camera parameters and lighting, on DL model performance on 3 vision tasks -- image classification, object detection, and visual question answering (VQA). To this end, we assess capture bias in common vision datasets and create a new benchmark, SNAP (for $\textbf{S}$hutter speed, ISO se$\textbf{N}$sitivity, and $\textbf{AP}$erture), consisting of images of objects taken under controlled lighting conditions and with densely sampled camera settings. We then evaluate a large number of DL vision models and show the effects of capture conditions on each selected vision task. Lastly, we conduct an experiment to establish a human baseline for the VQA task. Our results show that computer vision datasets are significantly biased, the models trained on this data do not reach human accuracy even on the well-exposed images, and are susceptible to both major exposure changes and minute variations of camera settings. Code and data can be found at https://github.com/ykotseruba/SNAP