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Graph-Based High-Order Relation Discovery for Fine-Grained Recognition
Metrics concerned:Accuracy
Pedestrian detection benefits from deep learning technology and gains rapid
development in recent years. Most of detectors follow general object detection
frame, i.e. default boxes and two-stage process. Recently, anchor-free and
one-stage detectors have been introduced into this area. However, their
accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of
anchor-free detectors and the accuracy of two-stage ones simultaneously, we
propose some adaptations based on a detector, Center and Scale Prediction(CSP).
The main contributions of our paper are: (1) We improve the robustness of CSP
and make it easier to train. (2) We propose a novel method to predict width,
namely compressing width. (3) We achieve the second best performance on
CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set,
8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and
one-stage detector can still have high accuracy. (4) We explore some
capabilities of Switchable Normalization which are not mentioned in its
original paper.
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