Due to the advantages of real-time detection and improved performance,
single-shot detectors have gained great attention recently. To solve the
complex scale variations, single-shot detectors make scale-aware predictions
based on multiple pyramid layers. However, the features in the pyramid are not
scale-aware enough, which limits the detection performance. Two common problems
in single-shot detectors caused by object scale variations can be observed: (1)
small objects are easily missed; (2) the salient part of a large object is
sometimes detected as an object. With this observation, we propose a new
Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid
features and explore scale-aware features. In NET, a Neighbor Erasing Module
(NEM) is designed to erase the salient features of large objects and emphasize
the features of small objects in shallow layers. A Neighbor Transferring Module
(NTM) is introduced to transfer the erased features and highlight large objects
in deep layers. With this mechanism, a single-shot network called NETNet is
constructed for scale-aware object detection. In addition, we propose to
aggregate nearest neighboring pyramid features to enhance our NET. NETNet
achieves 38.5% AP at a speed of 27 FPS and 32.0% AP at a speed of 55 FPS on MS
COCO dataset. As a result, NETNet achieves a better trade-off for real-time and
accurate object detection.

为了解决单次检测算法在目标尺度变化时的失效问题，本文提出一种名为 Neighbor Erasing and Transferring (NET) 的机制，实现对尺度感知特征的生成，同时借助该方法，构建了一种名为 NETNet 的单次检测网络，能够在保持实时性的前提下，取得更高的检测精度。

NETNet: 邻居擦除与转移网络用于更好的单次检测

NETNet: Neighbor Erasing and Transferring Network for Better Single Shot  Object Detection

We motivate and present feature selective anchor-free (FSAF) module, a simple
and effective building block for single-shot object detectors. It can be
plugged into single-shot detectors with feature pyramid structure. The FSAF
module addresses two limitations brought up by the conventional anchor-based
detection: 1) heuristic-guided feature selection; 2) overlap-based anchor
sampling. The general concept of the FSAF module is online feature selection
applied to the training of multi-level anchor-free branches. Specifically, an
anchor-free branch is attached to each level of the feature pyramid, allowing
box encoding and decoding in the anchor-free manner at an arbitrary level.
During training, we dynamically assign each instance to the most suitable
feature level. At the time of inference, the FSAF module can work jointly with
anchor-based branches by outputting predictions in parallel. We instantiate
this concept with simple implementations of anchor-free branches and online
feature selection strategy. Experimental results on the COCO detection track
show that our FSAF module performs better than anchor-based counterparts while
being faster. When working jointly with anchor-based branches, the FSAF module
robustly improves the baseline RetinaNet by a large margin under various
settings, while introducing nearly free inference overhead. And the resulting
best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing
single-shot detectors on COCO.

本文提出了一种基于特征选择锚点的：Feature Selective Anchor-Free (FSAF) 模块，它是一种可以插入特征金字塔结构的单镜头目标检测器构建模块，通过多级无锚点分支来在线选择特征以训练模型，并具有较高的检测准确性和速度。实验结果表明，FSAF 模块与 RetinaNet 效果相比显著提高，在 COCO 数据集上实现了 44.6% 的 mAP，成为目前所有现有单镜头检测器中表现最佳的模型。

面向单阶段目标检测的特征选择无锚点模块

Feature Selective Anchor-Free Module for Single-Shot Object Detection

Recently two-stage detectors have surged ahead of single-shot detectors in
the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are
immensely popular in embedded vision applications. This paper brings
single-shot detectors up to the same level as current two-stage techniques. We
do this by improving training for the state-of-the-art single-shot detector,
RetinaNet, in three ways: integrating instance mask prediction for the first
time, making the loss function adaptive and more stable, and including
additional hard examples in training. We call the resulting augmented network
RetinaMask. The detection component of RetinaMask has the same computational
cost as the original RetinaNet, but is more accurate. COCO test-dev results are
up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the
runtime is the same during evaluation. Adding Group Normalization increases the
performance of RetinaMask-101 to 41.7 mAP. Code is
at:this https URL

本文提出一种改进的单发检测器 RetinaMask，通过集成实例掩码预测、改进稳定的损失函数和增加训练时的困难样本等方式，将单发检测器的检测精度提高到和两阶段检测器相同的水平，COCO 测试结果显示，在保持相同运行时间的情况下，RetinaMask-101 模型的平均精度值 mAP 达到了 41.4，高于 RetinaNet-101 模型的 39.1。