Spatio-Temporal Ranked-Attention Networks for Video Captioning

• URL: http://arxiv.org/abs/2001.06127v1
• Date: Fri, 17 Jan 2020 01:00:45 GMT
• Title: Spatio-Temporal Ranked-Attention Networks for Video Captioning
• Authors: Anoop Cherian, Jue Wang, Chiori Hori, Tim K. Marks
• Abstract summary: We propose a model that combines spatial and temporal attention to videos in two different orders. We provide experiments on two benchmark datasets: MSVD and MSR-VTT. Our results demonstrate the synergy between the ST and TS modules, outperforming recent state-of-the-art methods.
• Score: 34.05025890230047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating video descriptions automatically is a challenging task that involves a complex interplay between spatio-temporal visual features and language models. Given that videos consist of spatial (frame-level) features and their temporal evolutions, an effective captioning model should be able to attend to these different cues selectively. To this end, we propose a Spatio-Temporal and Temporo-Spatial (STaTS) attention model which, conditioned on the language state, hierarchically combines spatial and temporal attention to videos in two different orders: (i) a spatio-temporal (ST) sub-model, which first attends to regions that have temporal evolution, then temporally pools the features from these regions; and (ii) a temporo-spatial (TS) sub-model, which first decides a single frame to attend to, then applies spatial attention within that frame. We propose a novel LSTM-based temporal ranking function, which we call ranked attention, for the ST model to capture action dynamics. Our entire framework is trained end-to-end. We provide experiments on two benchmark datasets: MSVD and MSR-VTT. Our results demonstrate the synergy between the ST and TS modules, outperforming recent state-of-the-art methods.

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