Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SCNet: Training Inference Sample Consistency for Instance Segmentation   Cascaded architectures have brought significant performance improvement in
object detection and instance segmentation. However, there are lingering issues
regarding the disparity in the Intersection-over-Union (IoU) distribution of
the samples between training and inference. This disparity can potentially
exacerbate detection accuracy. This paper proposes an architecture referred to
as Sample Consistency Network (SCNet) to ensure that the IoU distribution of
the samples at training time is close to that at inference time. Furthermore,
SCNet incorporates feature relay and utilizes global contextual information to
further reinforce the reciprocal relationships among classifying, detecting,
and segmenting sub-tasks. Extensive experiments on the standard COCO dataset
reveal the effectiveness of the proposed method over multiple evaluation
metrics, including box AP, mask AP, and inference speed. In particular, while
running 38\% faster, the proposed SCNet improves the AP of the box and mask
predictions by respectively 1.3 and 2.3 points compared to the strong Cascade
Mask R-CNN baseline. Code is available at
\url{https://github.com/thangvubk/SCNet}.

31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn