MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image.

This work presents a novel transformer-based method for hand pose estimation-DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propose a novel multi-stage approach from full-scene depth image-MuTr.

One Model is Not Enough: Ensembles for Isolated Sign Language Recognition.

In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e.