CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment

• URL: http://arxiv.org/abs/2404.13999v1
• Date: Mon, 22 Apr 2024 09:03:21 GMT
• Title: CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment
• Authors: Kanglei Zhou, Junlin Li, Ruizhi Cai, Liyuan Wang, Xingxing Zhang, Xiaohui Liang,
• Abstract summary: Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scale action recognition datasets to boost performance on smaller AQA datasets. We propose Coarse-to-Fine Instruction Alignment (CoFInAl) to align AQA with broader pre-trained tasks by reformulating it as a coarse-to-fine classification task.
• Score: 38.12600984070689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scale action recognition datasets to boost performance on smaller AQA datasets. However, this common strategy yields suboptimal results due to the inherent struggle of these backbones to capture the subtle cues essential for AQA. Moreover, fine-tuning on smaller datasets risks overfitting. To address these issues, we propose Coarse-to-Fine Instruction Alignment (CoFInAl). Inspired by recent advances in large language model tuning, CoFInAl aligns AQA with broader pre-trained tasks by reformulating it as a coarse-to-fine classification task. Initially, it learns grade prototypes for coarse assessment and then utilizes fixed sub-grade prototypes for fine-grained assessment. This hierarchical approach mirrors the judging process, enhancing interpretability within the AQA framework. Experimental results on two long-term AQA datasets demonstrate CoFInAl achieves state-of-the-art performance with significant correlation gains of 5.49% and 3.55% on Rhythmic Gymnastics and Fis-V, respectively. Our code is available at https://github.com/ZhouKanglei/CoFInAl_AQA.

Related papers

• Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
  Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
  arXiv  Detail & Related papers  (2024-06-26T05:30:21Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• Continual Action Assessment via Task-Consistent Score-Discriminative Feature Distribution Modeling [31.696222064667243]
  Action Quality Assessment (AQA) is a task that tries to answer how well an action is carried out. Existing works on AQA assume that all the training data are visible for training at one time, but do not enable continual learning. We propose a unified model to learn AQA tasks sequentially without forgetting.
  arXiv  Detail & Related papers  (2023-09-29T10:06:28Z)
• SAM-IQA: Can Segment Anything Boost Image Quality Assessment? [32.10446341968312]
  Deep learning-based IQA methods typically rely on pre-trained networks trained on massive datasets as feature extractors. In this paper, we utilize the encoder of Segment Anything, a recently proposed segmentation model trained on a massive dataset, for high-level semantic feature extraction. Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks.
  arXiv  Detail & Related papers  (2023-07-10T10:07:11Z)
• ReAct: Temporal Action Detection with Relational Queries [84.76646044604055]
  This work aims at advancing temporal action detection (TAD) using an encoder-decoder framework with action queries. We first propose a relational attention mechanism in the decoder, which guides the attention among queries based on their relations. Lastly, we propose to predict the localization quality of each action query at inference in order to distinguish high-quality queries.
  arXiv  Detail & Related papers  (2022-07-14T17:46:37Z)
• AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems [102.95119281306893]
  We present an early trial to explore adversarial training methods to optimize AUC. We reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem.
  arXiv  Detail & Related papers  (2022-06-24T09:13:39Z)
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need [146.6814176602689]
  We show that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes. We use this correlation to boost the performance of the cross-entropy OSR 'baseline' by improving its closed-set accuracy. We also construct new benchmarks which better respect the task of detecting semantic novelty.
  arXiv  Detail & Related papers  (2021-10-12T17:58:59Z)
• Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder [23.860842627883187]
  We teach the model to capture broader variations of the feature distributions with a novel noise-enhanced supervised autoencoder (NSAE) NSAE trains the model by jointly reconstructing inputs and predicting the labels of inputs as well as their reconstructed pairs. We also take advantage of NSAE structure and propose a two-step fine-tuning procedure that achieves better adaption and improves classification performance in the target domain.
  arXiv  Detail & Related papers  (2021-08-11T04:45:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.