Improving Action Quality Assessment using Weighted Aggregation

TL;DR

This paper introduces a weighted-averaging technique called Weight-Decider for better aggregation of clip features in action quality assessment, achieving state-of-the-art correlation scores on the MTL-AQA dataset.

Contribution

It proposes a learning-based weighted aggregation method (Weight-Decider) and evaluates ResNet architectures for improved action quality assessment.

Findings

Weighted-averaging improves performance over simple averaging.

ResNet-based features enhance action score prediction accuracy.

Achieved new state-of-the-art Spearman's rank correlation of 0.9315.

Abstract

Action quality assessment (AQA) aims at automatically judging human action based on a video of the said action and assigning a performance score to it. The majority of works in the existing literature on AQA divide RGB videos into short clips, transform these clips to higher-level representations using Convolutional 3D (C3D) networks, and aggregate them through averaging. These higher-level representations are used to perform AQA. We find that the current clip level feature aggregation technique of averaging is insufficient to capture the relative importance of clip level features. In this work, we propose a learning-based weighted-averaging technique. Using this technique, better performance can be obtained without sacrificing too much computational resources. We call this technique Weight-Decider(WD). We also experiment with ResNets for learning better representations for action quality assessment. We assess the effects of the depth and input clip size of the convolutional neural network on the quality of action score predictions. We achieve a new state-of-the-art Spearman's rank correlation of 0.9315 (an increase of 0.45%) on the MTL-AQA dataset using a 34 layer (2+1)D ResNet with the capability of processing 32 frame clips, with WD aggregation.