TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering

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TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering
Title:
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering
Journal Title:
ECCV 2020
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Publication Date:
28 August 2020
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Abstract:
Compositional visual question answering requires reasoning over both semantic and geometry object relations. We propose a novel tiered reasoning method that dynamically selects object level candidates based on language representations and generates robust pairwise rela- tions within the selected candidate objects. The proposed tiered relation reasoning method can be compatible with the majority of the existing visual reasoning frameworks, leading to signi cant performance improve- ment with very little extra computational cost. Moreover, we propose a policy network that decides the appropriate reasoning steps based on question complexity and current reasoning status. In experiments, our model achieves state-of-the-art performance on two VQA datasets.
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Funding Info:
This research was supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003) and the MOE Tier-1 research grants: RG28/18 (S) and RG22/19 (S). F. Lv’s participation is supported by National Natural Science Foundation of China (No. 11829101 and 11931014).
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