{'fraction_non_alphanumeric': 0.018551236749116608, 'fraction_numerical': 0.0, 'mean_word_length': 5.329608938547486, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Temporal Attention and Consistency Measuring for Video Question Answering

Social signal processing algorithms have become increasingly better at solving well-defined prediction and estimation problems in audiovisual recordings of group discussion. However, much human behavior and communication is less structured and more subtle. In this paper, we address the problem of generic question answering from diverse audiovisual recordings of human interaction. The goal is to select the correct free-text answer to a free-text question about human interaction in a video. We propose an RNN-based model with two novel ideas: a temporal attention module that highlights key words and phrases in the question and candidate answers, and a consistency measurement module that scores the similarity between the multimodal data, the question, and the candidate answers. This small set of consistency scores forms the input to the final question-answering stage, resulting in a lightweight model. We demonstrate that our model achieves state of the art accuracy on the Social-IQ dataset containing hundreds of videos and question/answer pairs.