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Deep convolutional models often produce inadequate predictions for inputsforeign to the training distribution. Consequently, the problem of detectingoutlier images has recently been receiving a lot of attention. Unlike mostprevious work, we address this problem in the dense prediction context in orderto be able to locate outlier objects in front of in-distribution background.Our approach is based on two reasonable assumptions. First, we assume that theinlier dataset is related to some narrow application field (e.g.~road driving).Second, we assume that there exists a general-purpose dataset which is muchmore diverse than the inlier dataset (e.g.~ImageNet-1k). We consider pixelsfrom the general-purpose dataset as noisy negative training samples since most(but not all) of them are outliers. We encourage the model to recognize bordersbetween known and unknown by pasting jittered negative patches over inliertraining images. Our experiments target two dense open-set recognitionbenchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognitiondataset (StreetHazard). Extensive performance evaluation indicates competitivepotential of the proposed approach.

Dense outlier detection and open-set recognition based on training with noisy negative images