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Robust video scene classification models should capture the spatial(pixel-wise) and temporal (frame-wise) characteristics of a video effectively.Transformer models with self-attention which are designed to get contextualizedrepresentations for individual tokens given a sequence of tokens, are becomingincreasingly popular in many computer vision tasks. However, the use ofTransformer based models for video understanding is still relativelyunexplored. Moreover, these models fail to exploit the strong temporalrelationships between the neighboring video frames to get potent frame-levelrepresentations. In this paper, we propose a novel self-attention block thatleverages both local and global temporal relationships between the video framesto obtain better contextualized representations for the individual frames. Thisenables the model to understand the video at various granularities. Weillustrate the performance of our models on the large scale YoutTube-8M dataset on the task of video categorization and further analyze the results toshowcase improvement.

Leveraging Local Temporal Information for Multimodal Scene Classification