Multi-Modal Low-Data-Based Learning for Video Classification

Abstract

Video classification is a challenging task in computer vision that requires analyzing the content of a video to assign it to one or more predefined categories. However, due to the vast amount of visual data contained in videos, the classification process is often computationally expensive and requires a significant amount of annotated data. Because of these reasons, the low-data-based video classification area, which consists of few-shot and zero-shot tasks, is proposed as a potential solution to overcome traditional video classification-oriented challenges. However, existing low-data area datasets, which are either not diverse or have no additional modality context, which is a mandatory requirement for the zero-shot task, do not fulfill the requirements for few-shot and zero-shot tasks completely. To address this gap, in this paper, we propose a large-scale, general-purpose dataset for the problem of multi-modal low-data-based video classification. The dataset contains pairs of videos and attributes that capture multiple facets of the video content. Thus, the new proposed dataset will both enable the study of low-data-based video classification tasks and provide consistency in terms of comparing the evaluations of future studies in this field. Furthermore, to evaluate and provide a baseline for future works on our new proposed dataset, we present a variational autoencoder-based model that leverages the inherent correlation among different modalities to learn more informative representations. In addition, we introduce a regularization technique to improve the baseline model's generalization performance in low-data scenarios. Our experimental results reveal that our proposed baseline model, with the aid of this regularization technique, achieves over 12% improvement in classification accuracy compared to the pure baseline model with only a single labeled sample.