  In this paper, we propose a framework centering around a novel architecture
called the Event Decomposition Recomposition Network (EDRNet) to tackle the
Audio-Visual Event (AVE) localization problem in the supervised and weakly
supervised settings. AVEs in the real world exhibit common unravelling patterns
(termed as Event Progress Checkpoints (EPC)), which humans can perceive through
the cooperation of their auditory and visual senses. Unlike earlier methods
which attempt to recognize entire event sequences, the EDRNet models EPCs and
inter-EPC relationships using stacked temporal convolutions. Based on the
postulation that EPC representations are theoretically consistent for an event
category, we introduce the State Machine Based Video Fusion, a novel
augmentation technique that blends source videos using different EPC template
sequences. Additionally, we design a new loss function called the
Land-Shore-Sea loss to compactify continuous foreground and background
representations. Lastly, to alleviate the issue of confusing events during weak
supervision, we propose a prediction stabilization method called Bag to
Instance Label Correction. Experiments on the AVE dataset show that our
collective framework outperforms the state-of-the-art by a sizable margin.
