As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensemble of classifiers. We train each classifier in a self-supervised manner by leaving out a random subset of training data as OOD data and the rest as in-distribution (ID) data. We propose a novel margin-based loss over the softmax output which seeks to maintain at least a margin m between the average entropy of the OOD and in-distribution samples. In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers. We also propose a novel method to combine the outputs of the ensemble of classifiers to obtain OOD detection score and class prediction. Overall, our method convincingly outperforms Hendrycks et al.[7] and the current state-of-the-art ODIN [13] on several OOD detection benchmarks.

Automatic identification and extraction of commercial blocks in telecast news videos find a lot of applications in the domain of broadcast monitoring. Existing works in this domain have used channel specific assumptions, machine learning techniques and frequentist approaches for detecting commercial video segments. We note that in the Indian context, several channel specific assumptions do not hold and often news and commercials have comparable frequencies of occurrence. This motivates us to use the machine learning techniques for classifying commercials in news videos. Our main contribution lies in the proposal of two features which are shown to outperform the existing audio-visual featuresfirst, the MFCC bag of words (BoW) as audio track feature and second, overlaid text distribution as video shot feature. The shot feature space is further extended by appending contextual features which are categorized by SVM based classifiers. Additionally, we have used a post-processing stage to suppress the false positives. We have experimented with 54 hours of video acquired from three different Indian English based news channels and have obtained a F-measure of around 97%.

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