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Found 6 papers, 4 papers with code
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components.
Enhanced fringe-to-phase framework using deep learning
In Fringe Projection Profilometry (FPP), achieving robust and accurate 3D reconstruction with a limited number of fringe patterns remains a challenge in structured light 3D imaging.
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition.
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.
AdamsFormer for Spatial Action Localization in the Future
SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately.
InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition
Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention.
Ranked #7 on
Skeleton Based Action Recognition
on N-UCLA
