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Found 8 papers, 0 papers with code
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial.
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks
Our research sheds light on the benefits of combining skeleton joints with object information for human action recognition in assembly tasks.
ATTACH Dataset: Annotated Two-Handed Assembly Actions for Human Action Understanding
With the emergence of collaborative robots (cobots), human-robot collaboration in industrial manufacturing is coming into focus.
A Little Bit Attention Is All You Need for Person Re-Identification
Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand.
Few-Shot Object Detection: A Comprehensive Survey
To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain.
Integrating Deep Learning in Domain Sciences at Exascale
This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e. g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP.
A scalable constructive algorithm for the optimization of neural network architectures
We propose a new scalable method to optimize the architecture of an artificial neural network.
Robust data-driven approach for predicting the configurational energy of high entropy alloys
In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys.
