SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection

• URL: http://arxiv.org/abs/2208.13930v5
• Date: Tue, 22 Aug 2023 21:33:48 GMT
• Title: SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
• Authors: Samuel Wilson, Tobias Fischer, Feras Dayoub, Dimity Miller and Niko S\"underhauf
• Abstract summary: We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) SAFE is consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract SAFE vectors for every detected object, and train a multilayer perceptron on the surrogate task of distinguishing adversarially perturbed from clean in-distribution examples.
• Score: 10.306996649145464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract SAFE vectors for every detected object, and train a multilayer perceptron on the surrogate task of distinguishing adversarially perturbed from clean in-distribution examples. This circumvents the need for realistic OOD training data, computationally expensive generative models, or retraining of the base object detector. SAFE outperforms the state-of-the-art OOD object detectors on multiple benchmarks by large margins, e.g. reducing the FPR95 by an absolute 30.6% from 48.3% to 17.7% on the OpenImages dataset.

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