A two-stage alignment framework that first fuses visual modalities (RGB, flow, skeleton) then introduces text, achieving 21% SRCC improvement on a new clinical AQA dataset and gains on two public benchmarks.
Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating
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abstract
The fine-grained action analysis of the existing action datasets is challenged by insufficient action categories, low fine granularities, limited modalities, and tasks. In this paper, we propose a Multi-modality and Multi-task dataset of Figure Skating (MMFS) which was collected from the World Figure Skating Championships. MMFS, which possesses action recognition and action quality assessment, captures RGB, skeleton, and is collected the score of actions from 11671 clips with 256 categories including spatial and temporal labels. The key contributions of our dataset fall into three aspects as follows. (1) Independently spatial and temporal categories are first proposed to further explore fine-grained action recognition and quality assessment. (2) MMFS first introduces the skeleton modality for complex fine-grained action quality assessment. (3) Our multi-modality and multi-task dataset encourage more action analysis models. To benchmark our dataset, we adopt RGB-based and skeleton-based baseline methods for action recognition and action quality assessment.
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Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment
A two-stage alignment framework that first fuses visual modalities (RGB, flow, skeleton) then introduces text, achieving 21% SRCC improvement on a new clinical AQA dataset and gains on two public benchmarks.
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