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Existing audio-visual event localization (AVE) handles manually trimmedvideos with only a single instance in each of them. However, this setting isunrealistic as natural videos often contain numerous audio-visual events withdifferent categories. To better adapt to real-life applications, in this paperwe focus on the task of dense-localizing audio-visual events, which aims tojointly localize and recognize all audio-visual events occurring in anuntrimmed video. The problem is challenging as it requires fine-grainedaudio-visual scene and context understanding. To tackle this problem, weintroduce the first Untrimmed Audio-Visual (UnAV-100) dataset, which contains10K untrimmed videos with over 30K audio-visual events. Each video has 2.8audio-visual events on average, and the events are usually related to eachother and might co-occur as in real-life scenes. Next, we formulate the taskusing a new learning-based framework, which is capable of fully integratingaudio and visual modalities to localize audio-visual events with variouslengths and capture dependencies between them in a single pass. Extensiveexperiments demonstrate the effectiveness of our method as well as thesignificance of multi-scale cross-modal perception and dependency modeling forthis task.

Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline