RICA2: Rubric-Informed, Calibrated Assessment of Actions
This addresses the problem of accurately evaluating action performance in vision tasks, offering a calibrated approach for applications like sports or surgical analysis, but it is incremental as it builds on prior AQA methods.
The paper tackles action quality assessment by integrating score rubrics and quantifying prediction uncertainty, achieving new state-of-the-art results on benchmarks like FineDiving, MTL-AQA, and JIGSAWS with superior score prediction and uncertainty calibration.
The ability to quantify how well an action is carried out, also known as action quality assessment (AQA), has attracted recent interest in the vision community. Unfortunately, prior methods often ignore the score rubric used by human experts and fall short of quantifying the uncertainty of the model prediction. To bridge the gap, we present RICA^2 - a deep probabilistic model that integrates score rubric and accounts for prediction uncertainty for AQA. Central to our method lies in stochastic embeddings of action steps, defined on a graph structure that encodes the score rubric. The embeddings spread probabilistic density in the latent space and allow our method to represent model uncertainty. The graph encodes the scoring criteria, based on which the quality scores can be decoded. We demonstrate that our method establishes new state of the art on public benchmarks, including FineDiving, MTL-AQA, and JIGSAWS, with superior performance in score prediction and uncertainty calibration. Our code is available at https://abrarmajeedi.github.io/rica2_aqa/