Luo, Lin, G., Yao, Y., Liu, F., Liu, Z., &amp; Tang, Z. (2022). Depth and Video Segmentation Based Visual Attention for Embodied Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3139957
Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user’s
questions by exploring the real-world environment. It has attracted increasing research interests due to its broad applications in
personal assistants and in-home robots. Most of the existing methods perform poorly in terms of answering and navigation accuracy
due to the absence of fine-level semantic information, stability to the ambiguity, and 3D spatial information of the virtual environment.
To tackle these problems, we propose a depth and segmentation based visual attention mechanism for Embodied Question Answering.
Firstly, we extract local semantic features by introducing a novel high-speed video segmentation framework. Then guided by the
extracted semantic features, a depth and segmentation based visual attention mechanism is proposed for the Visual Question
Answering (VQA) sub-task. Further, a feature fusion strategy is designed to guide the navigator’s training process without much
additional computational cost. The ablation experiments show that our method effectively boosts the performance of the VQA module
and navigation module, leading to 4.9% and 5.6% overall improvement in EQA accuracy on House3D and Matterport3D datasets
This research / project is supported by the National Research Foundation (NRF) - AI Singapore Programme
Grant Reference no. : AISG-RP-2018-003
This research / project is supported by the Ministry of Education (MOE) - Tier-1 research grants
Grant Reference no. : RG28/18 (S), RG22/19 (S) and RG95/20
It is also supported by the National Natural Science Foundation of China (No.62102182, 61976116, and 61905114), Natural Science Foundation of Jiangsu Province (No. BK20210327), Fundamental Research Funds for the Central Universities (No. 30920021135), and the National Key R&D Program of China (2021YFF0602101).