Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Model Calibration for Long-Tailed Object Detection and Instance
  Segmentation   Vanilla models for object detection and instance segmentation suffer from the
heavy bias toward detecting frequent objects in the long-tailed setting.
Existing methods address this issue mostly during training, e.g., by
re-sampling or re-weighting. In this paper, we investigate a largely overlooked
approach -- post-processing calibration of confidence scores. We propose
NorCal, Normalized Calibration for long-tailed object detection and instance
segmentation, a simple and straightforward recipe that reweighs the predicted
scores of each class by its training sample size. We show that separately
handling the background class and normalizing the scores over classes for each
proposal are keys to achieving superior performance. On the LVIS dataset,
NorCal can effectively improve nearly all the baseline models not only on rare
classes but also on common and frequent classes. Finally, we conduct extensive
analysis and ablation studies to offer insights into various modeling choices
and mechanisms of our approach. Our code is publicly available at
https://github.com/tydpan/NorCal/.

14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
18
The categories of this paper are: econ
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol