Action Quality Assessment (AQA) has wide applications in various scenarios. Regarding the AQA of long-term figure skating, the big challenge lies in semantic context feature learning for Program Component Score (PCS) prediction and fine-grained technical subaction analysis for Technical Element Score (TES) prediction. In this paper, we propose a Localization-assisted Uncertainty Score Disentanglement Network (LUSD-Net) to deal with PCS and TES two predictions. In the LUSD-Net, we design an uncertainty score disentanglement solution, including score disentanglement and uncertainty regression, to decouple PCS-oriented and TES-oriented representations from skating sequences, ensuring learning differential representations for two types of score prediction. For long-term feature learning, a temporal interaction encoder is presented to build temporal context relation learning on PCS-oriented and TES-oriented features. To address subactions in TES prediction, a weakly-supervised temporal subaction localization is adopted to locate technical subactions in long sequences. For evaluation, we collect a large-scale Fine-grained Figure Skating dataset (FineFS) involving RGB videos and estimated skeleton sequences, providing rich annotations for multiple downstream action analysis tasks. The extensive experiments illustrate that our proposed LUSD-Net significantly improves the AQA performance, and the FineFS dataset provides a quantity data source for the AQA. The source code of LUSD-Net and the FineFS dataset is released at https://github.com/yanliji/FineFS-dataset.

Recent deep learning approaches have achieved impressive performance in visually-guided sound source separation tasks. However, due to the lack of real-world mixed/separated audio sample pairs, most methods seriously rely on the "Mix-and-Separate" manner to learn sound source separation, often unsuitable for real-world mixtures. To address this issue, we utilize a semi-supervised learning technique — preserving audio-visual consistency — to improve the separation performance of real-world scenarios. In this way, our network is trained jointly by artificial and real-world mixtures. To the best of our knowledge, this could be the first attempt to improve real-world generalization. We also design a category-guided audiovisual fusion module to learn audio-visual matching. Comparative experiments are performed on two publicly-available datasets, MUSIC and AudioSet. Experiment results demonstrate that our method could often outperform other state-of-the-art ones in visual sound separation.

View change brings a significant challenge to action representation and recognition due to pose occlusion and deformation. We propose a Global-Local Cross-View Fisher Discrimination (GL-CVFD) algorithm to tackle this problem. In the GL-CVFD approach, we firstly capture the motion trajectory of body joints in action sequences as feature input to weaken the effect of view change. Secondly, we design a Global-Local Cross-View Representation (CVR) learning module, which builds global-level and local-level graphs to link body parts and joints between different views. It can enhance the cross-view information interaction and obtain an effective view-common action representation. Thirdly, we present a Cross-View Fisher Discrimination (CVFD) module, which performs a view-differential operation to separate view-specific action features and modifies the Fisher discriminator to implement view-semantic Fisher contrastive learning. It operates by pulling and pushing on view-specific and view-common action features in the view term to guarantee the validity of the CVR module, then distinguishes view-common action features in the semantic term for view-invariant recognition. Extensive and fair evaluations are implemented in the UESTC, NTU 60, and NTU 120 datasets. Experiment results illustrate that our proposed approach achieves encouraging performance in skeleton-based view-invariant action recognition.

Since the human body is non-rigid, actions captured in different views always involve action occlusion and information loss. Recently, view-variation-related human action recognition is still a challenging problem. To address the problem, we propose a View Transformation Network (VTN) that realizes the view normalization by transforming arbitrary-view action samples to a base view to seek for a view-invariant representation. an attention learning module is designed to learn a co-attention for action samples of different views, that contributes to output a similar feature representation to erase the view diversity in different views. Extensive and fair evaluations are performed on the UESTC varying-view RGB-D dataset, the NTU RGB-D 60 dataset, and the NTU RGB-D 120 dataset, where three evaluation types,i.e.X-subject, X-view, and A-view recognition, are performed. Experiments illustrate that our VTN model achieves outstanding performance.

Modern distributed storage systems provide large-scale, fault-tolerant data storage. To reduce the probability of data unavailability, it is important to recover the lost data of any failed storage node efficiently. In practice, storage nodes are of heterogeneous types and have different transmission bandwidths. Thus, traditional recovery solutions that simply minimize the number of data blocks being read may no longer be optimal in a heterogeneous environment. We propose a cost-based heterogeneous recovery (CHR) algorithm for RAID-6-coded storage systems. We formulate the recovery problem as an optimization model in which storage nodes are associated with generic costs. We narrow down the solution space of the model to make it practically tractable, while still achieving the global optimal solution in most cases. We implement different recovery algorithms and conduct testbed experiments on a real networked storage system with heterogeneous storage devices. We show that our CHR algorithm reduces the total recovery time of existing recovery solutions in various scenarios.