Occlusion between objects is one of the overlooked challenges for object
detection in UAV images. Due to the variable altitude and angle of UAVs,
occlusion in UAV images happens more frequently than that in natural scenes.
Compared to occlusion in natural scene images, occlusion in UAV images happens
with feature confusion problem and local aggregation characteristic. And we
found that extracting or localizing occlusion between objects is beneficial for
the detector to address this challenge. According to this finding, the
occlusion localization task is introduced, which together with the object
detection task constitutes our occlusion-guided multi-task network (OGMN). The
OGMN contains the localization of occlusion and two occlusion-guided multi-task
interactions. In detail, an occlusion estimation module (OEM) is proposed to
precisely localize occlusion. Then the OGMN utilizes the occlusion localization
results to implement occlusion-guided detection with two multi-task
interactions. One interaction for the guide is between two task decoders to
address the feature confusion problem, and an occlusion decoupling head (ODH)
is proposed to replace the general detection head. Another interaction for
guide is designed in the detection process according to local aggregation
characteristic, and a two-phase progressive refinement process (TPP) is
proposed to optimize the detection process. Extensive experiments demonstrate
the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In
particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms
the baseline by 5.3%. And our OGMN provides a new insight for accurate
occlusion localization and achieves competitive detection performance.

介绍了一个名为 OGMN 的多任务网络，用于在 UAV 图像中检测物体。通过引入遮挡定位任务，OGMN 能够克服 UAV 图像中存在的遮挡问题和特征混淆问题，并通过两个多任务交互来进行遮挡引导检测。通过在 Visdrone 和 UAVDT 数据集上进行大量实验，证明了 OGMN 的有效性，并提供了精确遮挡定位的新见解。

OGMN：基于遮挡引导的多任务网络用于无人机图像物体检测

OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV  Images

Lately, generative adversarial networks (GANs) have been successfully applied
to zero-shot learning (ZSL) and achieved state-of-the-art performance. By
synthesizing virtual unseen visual features, GAN-based methods convert the
challenging ZSL task into a supervised learning problem. However, GAN-based ZSL
methods have to train the generator on the seen categories and further apply it
to unseen instances. An inevitable issue of such a paradigm is that the
synthesized unseen features are prone to seen references and incapable to
reflect the novelty and diversity of real unseen instances. In a nutshell, the
synthesized features are confusing. One cannot tell unseen categories from seen
ones using the synthesized features. As a result, the synthesized features are
too subtle to be classified in generalized zero-shot learning (GZSL) which
involves both seen and unseen categories at the test stage. In this paper, we
first introduce the feature confusion issue. Then, we propose a new feature
generating network, named alleviating feature confusion GAN (AFC-GAN), to
challenge the issue. Specifically, we present a boundary loss which maximizes
the decision boundary of seen categories and unseen ones. Furthermore, a novel
metric named feature confusion score (FCS) is proposed to quantify the feature
confusion. Extensive experiments on five widely used datasets verify that our
method is able to outperform previous state-of-the-arts under both ZSL and GZSL
protocols.

本文提出了一种新的特征生成网络 AFC-GAN，用于解决基于 GAN 的零样本学习中存在的特征混淆问题，通过提出边界损失和特征混淆得分指标 FCS 等方法可以显著提高模型的性能。