One-Shot Learning for Real-Time Action Recognition
By Sean Ryan Fanello, Ilaria Gori, Giorgio Metta, Francesca Odone. 2013
32 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Meta & Continual Learning, Human-Computer Interaction
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Citations in the corpus
- NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, methodology): “Fanello et al. [90] used 3D-HOF and Global-HOG features for one-shot action recognition in RGB+D videos.”
- One-shot-learning gesture recognition using HOG-HOF features (2013, methodology): “Fanello et al. (2013) develop a real-time learning and recognition system for RGB-D images.”
- Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, methodology): “However, in [19], [29], [33], [35], [36], the HOG or HOF features are extracted globally because they are directly computed from RGB or depth frames.”
- Multi-layered gesture recognition with Kinect (2015, methodology): “, 2013), linear SVMs (Fanello et al., 2013), neuro-fuzzy inference system networks (AlJarrah and Halawani, 2001), hyper rectangular composite NNs (Su, 2000), and 3D Hopfield NN (Huang and Huang, 1998).”
- StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction (2018, background): “Active depth sensors like the Microsoft Kinect provide high quality depthmaps and have not only revolutionized computer vision research [12, 11, 41, 16, 55], but also play an important role in consumer level applications.”