Shape-Adaptive Selection and Measurement for Oriented Object Detection

Last update: Nov 29, 2022

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Overview

Source Code of AAAI22-2171

Introduction

The source code includes training and inference procedures for the proposed method of the paper submitted to the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022) with title "Shape-Adaptive Selection and Measurement for Oriented Object Detection" (ID: 2171).

The the effectiveness of the proposed method is verified on two baseline methods. Corresponding source code and configurations reside in following two sub-directories:

sasm_reppoints: the implementation and verification using RepPoints as baseline;
sasm_s2anet: the implementation and verification using s2anet(https://ieeexplore.ieee.org/document/9377550) as baseline.

We provide only the source code related to the proposed method in the sub-directories so that reviewers can check them quickly and conveniently.

Please refer to the README.md file in each sub-directory for the detailed instructions of usage.

Introduction

Method	Assignment	Reg. Loss	Tricks	mAP
RepPoints	MaxIoU	GIoU	-	70.46
RepPoints	SASM	BCLoss + GIoU	-	74.27
RepPoints	SASM	BCLoss + GIoU	MS training	77.19
s2anet	SASM	Smooth L1	MS training	79.17

Reference

1、https://github.com/open-mmlab/mmdetection

2、https://github.com/LiWentomng/OrientedRepPoints

3、https://github.com/SDL-GuoZonghao/BeyondBoundingBox

4、https://github.com/csuhan/s2anet

5、https://github.com/sfzhang15/ATSS

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Comments

SA-M strategy

Hi, I found that the formula for calculating distance in the code is different from that in the paper. Is the effect of using the formula in the code similar to that in the paper? Also,why is the formula for calculating distance different in [0,90] and other angles?

opened by heyun1994 5

Shape-Adaptive Selection and Measurement for Oriented Object Detection

Related tags

Overview

Source Code of AAAI22-2171

Introduction

Introduction

Reference

You might also like...

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)

Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection

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Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

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Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Comments

SA-M strategy

Owner

houliping

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