Leveraging Local Temporal Information for Multimodal Scene Classification

TL;DR

This paper introduces a novel self-attention block that captures local and global temporal relationships in videos, improving scene classification by better understanding frame-level context, especially on large-scale datasets.

Contribution

It proposes a new self-attention mechanism that exploits local and global temporal information for enhanced video understanding in transformer models.

Findings

Improved accuracy on YouTube-8M dataset.

Effective modeling of local and global temporal relationships.

Enhanced frame-level representations for video classification.

Abstract

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 contextualized representations for individual tokens given a sequence of tokens, are becoming increasingly popular in many computer vision tasks. However, the use of Transformer based models for video understanding is still relatively unexplored. Moreover, these models fail to exploit the strong temporal relationships between the neighboring video frames to get potent frame-level representations. In this paper, we propose a novel self-attention block that leverages both local and global temporal relationships between the video frames to obtain better contextualized representations for the individual frames. This enables the model to understand the video at various granularities. We illustrate the performance of our models on the large scale YoutTube-8M data set on the task of video categorization and further analyze the results to showcase improvement.