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In this paper, we newly introduce the concept of temporal attention filters,and describe how they can be used for human activity recognition from videos.Many high-level activities are often composed of multiple temporal parts (e.g.,sub-events) with different duration/speed, and our objective is to make themodel explicitly learn such temporal structure using multiple attention filtersand benefit from them. Our temporal filters are designed to be fullydifferentiable, allowing end-of-end training of the temporal filters togetherwith the underlying frame-based or segment-based convolutional neural networkarchitectures. This paper presents an approach of learning a set of optimalstatic temporal attention filters to be shared across different videos, andextends this approach to dynamically adjust attention filters per testing videousing recurrent long short-term memory networks (LSTMs). This allows ourtemporal attention filters to learn latent sub-events specific to eachactivity. We experimentally confirm that the proposed concept of temporalattention filters benefits the activity recognition, and we visualize thelearned latent sub-events.

Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters