Character Prediction in TV Series via a Semantic Projection Network

The goal of this paper is to automatically recognize characters in popular TV series. In contrast to conventional approaches which rely on weak supervision afforded by transcripts, subtitles or character facial data, we formulate the problem as the multi-label classification which requires only label-level supervision. We propose a novel semantic projection network consisting of two stacked subnetworks with specially designed constraints. The first subnetwork is a contractive autoencoder which focuses on reconstructing feature activations extracted from a pre-trained single-label convolutional neural network (CNN). The second subnetwork functions as a region-based multi-label classifier which produces character labels for the input video frame as well as reconstructing the input visual feature from the mapped semantic labels space. Extensive experiments show that the proposed model achieves state-of-the-art performance in comparison with recent approaches on three challenging TV series datasets (the Big Bang Theory, the Defenders and Nirvava in Fire).