SaccadeNet: A Fast and Accurate Object Detector

CVPR 2020  ·  Shiyi Lan, Zhou Ren, Yi Wu, Larry S. Davis, Gang Hua ·

Object detection is an essential step towards holistic scene understanding. Most existing object detection algorithms attend to certain object areas once and then predict the object locations. However, neuroscientists have revealed that humans do not look at the scene in fixed steadiness. Instead, human eyes move around, locating informative parts to understand the object location. This active perceiving movement process is called \textit{saccade}. %In this paper, Inspired by such mechanism, we propose a fast and accurate object detector called \textit{SaccadeNet}. It contains four main modules, the \cenam, the \coram, the \atm, and the \aggatt, which allows it to attend to different informative object keypoints, and predict object locations from coarse to fine. The \coram~is used only during training to extract more informative corner features which brings free-lunch performance boost. On the MS COCO dataset, we achieve the performance of 40.4\% mAP at 28 FPS and 30.5\% mAP at 118 FPS. Among all the real-time object detectors, %that can run faster than 25 FPS, our SaccadeNet achieves the best detection performance, which demonstrates the effectiveness of the proposed detection mechanism.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Object Detection COCO test-dev SaccadeNet (DLA-34-DCN) box mAP 38.5 # 205
AP50 55.6 # 151
AP75 41.4 # 146
APS 19.2 # 137
APM 42.1 # 131
APL 50.6 # 136
Hardware Burden 46G # 1
Operations per network pass None # 1

Methods


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