Action quality assessment (AQA) applies computer vision to quantitatively
assess the performance or execution of a human action. Current AQA approaches
are end-to-end neural models, which lack transparency and tend to be biased
because they are trained on subjective human judgements as ground-truth. To
address these issues, we introduce a neuro-symbolic paradigm for AQA, which
uses neural networks to abstract interpretable symbols from video data and
makes quality assessments by applying rules to those symbols. We take diving as
the case study. We found that domain experts prefer our system and find it more
informative than purely neural approaches to AQA in diving. Our system also
achieves state-of-the-art action recognition and temporal segmentation, and
automatically generates a detailed report that breaks the dive down into its
elements and provides objective scoring with visual evidence. As verified by a
group of domain experts, this report may be used to assist judges in scoring,
help train judges, and provide feedback to divers. We will open-source all of
our annotated training data and code for ease of reproducibility.

使用计算机视觉进行行动质量评估的研究通过神经网络从视频数据中提取可解释的符号，并应用规则进行质量评估。该研究以潜水为案例研究，发现领域专家更喜欢该系统，并认为它比纯粹的神经网络方法在潜水行动质量评估方面更具信息量。该系统还实现了先进的动作识别和时间分割，并自动生成详细报告，将潜水行动分解为各个元素，并提供带有可视证据的客观评分。通过一组领域专家的验证，该报告可以用于协助评委打分，培训评委，并向潜水员提供反馈。研究人员将公开其所有的标注训练数据和代码以便于可重复性。

嵌套神经符号法对动作质量评估的研究

Hierarchical NeuroSymbolic Approach for Action Quality Assessment

Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often
solve large real-world problems within minutes. This success can partially be
attributed to heuristics. Since their behavior is highly instance-dependent,
relying on hard-coded rules derived from empirical testing on a large
heterogeneous corpora of benchmark instances might lead to sub-optimal
performance. In this work, we propose an online learning approach that adapts
the application of heuristics towards the single instance at hand. We replace
the commonly used static heuristic handling with an adaptive framework
exploiting past observations about the heuristic's behavior to make future
decisions. In particular, we model the problem of controlling Large
Neighborhood Search and Diving - two broad and complex classes of heuristics -
as a multi-armed bandit problem. Going beyond existing work in the literature,
we control two different classes of heuristics simultaneously by a single
learning agent. We verify our approach numerically and show consistent node
reductions over the MIPLIB 2017 Benchmark set. For harder instances that take
at least 1000 seconds to solve, we observe a speedup of 4%.

本文提出了一种在线学习方法，用于优化 Mixed Integer Programming (MIP) 中的启发式算法，以适应于不同的实例，并将大邻域搜索和 Diving 两类复杂启发式方法同时控制，实现 MIPLIB 2017 基准测试的节点数减少和速度提升。