Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Siamese Regression Networks with Efficient mid-level Feature Extraction
  for 3D Object Pose Estimation   In this paper we tackle the problem of estimating the 3D pose of object
instances, using convolutional neural networks. State of the art methods
usually solve the challenging problem of regression in angle space indirectly,
focusing on learning discriminative features that are later fed into a separate
architecture for 3D pose estimation. In contrast, we propose an end-to-end
learning framework for directly regressing object poses by exploiting Siamese
Networks. For a given image pair, we enforce a similarity measure between the
representation of the sample images in the feature and pose space respectively,
that is shown to boost regression performance. Furthermore, we argue that our
pose-guided feature learning using our Siamese Regression Network generates
more discriminative features that outperform the state of the art. Last, our
feature learning formulation provides the ability of learning features that can
perform under severe occlusions, demonstrating high performance on our novel
hand-object dataset.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci