High-Level Semantic Networks for Multi-Scale Object Detection [MHN]

Submitted on 15 Jan. 2019 16:03 by
Jiale Cao (Tianjin)

Running time:0.39 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
we propose a multi-branch and high-level semantic
network by gradually splitting a base network into
multiple different branches.
see the paper:
Latex Bibtex:
title={High-Level Semantic Networks for Multi-
Scale Object Detection},
author={Jiale Cao and Yanwei Pang and Shengjie
Zhao and Xuelong Li},
booktitle={IEEE Transactions on Circuits and
Systems for Video Technology},

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).

Benchmark Easy Moderate Hard
Pedestrian (Detection) 87.21 % 75.99 % 69.50 %
This table as LaTeX

2D object detection results.
This figure as: png eps pdf txt gnuplot

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