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In this paper, we propose a framework centering around a novel architecturecalled the Event Decomposition Recomposition Network (EDRNet) to tackle theAudio-Visual Event (AVE) localization problem in the supervised and weaklysupervised settings. AVEs in the real world exhibit common unravelling patterns(termed as Event Progress Checkpoints (EPC)), which humans can perceive throughthe cooperation of their auditory and visual senses. Unlike earlier methodswhich attempt to recognize entire event sequences, the EDRNet models EPCs andinter-EPC relationships using stacked temporal convolutions. Based on thepostulation that EPC representations are theoretically consistent for an eventcategory, we introduce the State Machine Based Video Fusion, a novelaugmentation technique that blends source videos using different EPC templatesequences. Additionally, we design a new loss function called theLand-Shore-Sea loss to compactify continuous foreground and backgroundrepresentations. Lastly, to alleviate the issue of confusing events during weaksupervision, we propose a prediction stabilization method called Bag toInstance Label Correction. Experiments on the AVE dataset show that ourcollective framework outperforms the state-of-the-art by a sizable margin.

Decompose the Sounds and Pixels, Recompose the Events