UniCon+: ICTCAS-UCAS Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2022
Yuanhang Zhang, Susan Liang, Shuang Yang, Shiguang Shan

TL;DR
This paper introduces UniCon+, a state-of-the-art active speaker detection model that builds on previous architectures with a GRU-based module, achieving top performance with 94.47% mAP at ActivityNet Challenge 2022.
Contribution
UniCon+ extends the Unified Context Network with a GRU module for better identity flow, setting new state-of-the-art results in active speaker detection.
Findings
Achieved 94.47% mAP on AVA-ActiveSpeaker test set.
Ranked first on the ActivityNet Challenge 2022 leaderboard.
Significantly improved over previous models.
Abstract
This report presents a brief description of our winning solution to the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2022. Our underlying model UniCon+ continues to build on our previous work, the Unified Context Network (UniCon) and Extended UniCon which are designed for robust scene-level ASD. We augment the architecture with a simple GRU-based module that allows information of recurring identities to flow across scenes through read and update operations. We report a best result of 94.47% mAP on the AVA-ActiveSpeaker test set, which continues to rank first on this year's challenge leaderboard and significantly pushes the state-of-the-art.
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Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing · Speech and dialogue systems
MethodsTest
