Joint Event Detection and Description in Continuous Video Streams

Dense video captioning involves first localizing events in a video and then generating captions for the identified events. We present the Joint Event Detection and Description Network (JEDDi-Net) for solving this task in an end-to-end fashion, which encodes the input video stream with three-dimensional convolutional layers, proposes variable- length temporal events based on pooled features, and then uses a two-level hierarchical LSTM module with context modeling to transcribe the event proposals into captions. We show the effectiveness of our proposed JEDDi-Net on the large-scale ActivityNet Captions dataset.