9 papers with code • 0 benchmarks • 4 datasets
These leaderboards are used to track progress in Hand Segmentation
We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture.
To overcome this challenge, we develop a neural network which is able to adapt the receptive field not only for each layer but also for each neuron at the spatial location.
Our model is built on the observation that egocentric activities are highly characterized by the objects and their locations in the video.
To this end, we propose a Bayesian CNN-based model adaptation framework for hand segmentation, which introduces and considers two key factors: 1) prediction uncertainty when the model is applied in a new domain and 2) common information about hand shapes shared across domains.
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
We validated our method on domain adaptation of hand segmentation from real and simulation images.
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout
Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout.