AI in EE

AI IN DIVISIONS

AI in Signal Division

AI in EE

AI IN DIVISIONS

AI in Signal Division ​ ​

AI in Signal Division

Junyeong Kim, Minuk Ma, Trung X, Kyungsu Kim and Chang D. Yoo, "Modality Shifting Attention Network for Multi-modal Video Question Answering", Computer Vision and Pattern Recognition, 2020.

This paper considers a network referred to as Modality Shifting Attention Network (MSAN) for Multimodal Video Question Answering (MVQA) task. MSAN decomposes the task into two sub-tasks: (1) localization of temporal moment relevant to the question, and (2) accurate prediction of the answer based on the localized moment. The modality required for temporal localization may be different from that for answer prediction, and this ability to shift modality is essential for performing the task. To this end, MSAN is based on (1) the moment proposal network (MPN) that attempts to locate the most appropriate temporal moment from each of the modalities, and also on (2) the heterogeneous reasoning network (HRN) that predicts the answer using an attention mechanism on both modalities. MSAN is able to place importance weight on the two modalities for each sub-task using a component referred to as Modality Importance Modulation (MIM). Experimental results show that MSAN outperforms previous state-of-the-art by achieving 71.13% test accuracy on TVQA benchmark dataset. Extensive ablation studies and qualitative analysis are conducted to validate various components of the network.

Figure 1. Illustration of modality shifting attention network (MSAN) which is composed of the following components: (a) Video and text representation utilizing BERT for embedding, (b) Moment proposal network to localize the required temporal moment of interest for answering the question, (c) Heterogeneous reasoning network to infer the correct answer based on the localized moment, and (d) Modality importance modulation to weight the output of (b) and of (c) differently according to their importance.