Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation   While hand pose estimation is a critical component of most interactive
extended reality and gesture recognition systems, contemporary approaches are
not optimized for computational and memory efficiency. In this paper, we
propose a tiny deep neural network of which partial layers are recursively
exploited for refining its previous estimations. During its iterative
refinements, we employ learned gating criteria to decide whether to exit from
the weight-sharing loop, allowing per-sample adaptation in our model. Our
network is trained to be aware of the uncertainty in its current predictions to
efficiently gate at each iteration, estimating variances after each loop for
its keypoint estimates. Additionally, we investigate the effectiveness of
end-to-end and progressive training protocols for our recursive structure on
maximizing the model capacity. With the proposed setting, our method
consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches
in terms of both accuracy and efficiency for widely used benchmarks.

7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org