Accurately ranking the vast number of candidate detections is crucial for
dense object detectors to achieve high performance. Prior work uses the
classification score or a combination of classification and predicted
localization scores to rank candidates. However, neither option results in a
reliable ranking, thus degrading detection performance. In this paper, we
propose to learn an Iou-aware Classification Score (IACS) as a joint
representation of object presence confidence and localization accuracy. We show
that dense object detectors can achieve a more accurate ranking of candidate
detections based on the IACS. We design a new loss function, named Varifocal
Loss, to train a dense object detector to predict the IACS, and propose a new
star-shaped bounding box feature representation for IACS prediction and
bounding box refinement. Combining these two new components and a bounding box
refinement branch, we build an IoU-aware dense object detector based on the
FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive
experiments on MS COCO show that our VFNet consistently surpasses the strong
baseline by $\sim$2.0 AP with different backbones. Our best model VFNet-X-1200
with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO
test-dev, which is state-of-the-art among various object detectors.Code is
available at this https URL .

本文提出了新的 Iou-aware Classification Score (IACS)、Varifocal Loss 以及 star-shaped bounding box feature，建立了基于 FCOS+ATSS 体系结构的 IoU-aware dense object detector VarifocalNet (VFNet)，在 MS COCO 数据集上进行了广泛实验，其效果表现出了明显的优势。

VarifocalNet: 一种 IoU 感知的密集物体检测器

VarifocalNet: An IoU-aware Dense Object Detector

We present an efficient 3D object detection framework based on a single RGB
image in the scenario of autonomous driving. Our efforts are put on extracting
the underlying 3D information in a 2D image and determining the accurate 3D
bounding box of the object without point cloud or stereo data. Leveraging the
off-the-shelf 2D object detector, we propose an artful approach to efficiently
obtain a coarse cuboid for each predicted 2D box. The coarse cuboid has enough
accuracy to guide us to determine the 3D box of the object by refinement. In
contrast to previous state-of-the-art methods that only use the features
extracted from the 2D bounding box for box refinement, we explore the 3D
structure information of the object by employing the visual features of visible
surfaces. The new features from surfaces are utilized to eliminate the problem
of representation ambiguity brought by only using a 2D bounding box. Moreover,
we investigate different methods of 3D box refinement and discover that a
classification formulation with quality aware loss has much better performance
than regression. Evaluated on the KITTI benchmark, our approach outperforms
current state-of-the-art methods for single RGB image based 3D object
detection.

本文提出了一种基于单个 RGB 图像的高效三维目标检测框架，旨在从二维图像中提取三维信息并在无点云或立体数据的情况下确定对象的精确三维边界框。该方法利用二维目标检测器提取表面视觉特征，消除使用二维边界框带来的表征歧义问题，并探索了不同的三维边界框细化方法，发现基于质量感知损失的分类式方法具有更好的性能。在 KITTI 基准测试上，该方法优于当前单个 RGB 图像基础的三维目标检测技术水平。

GS3D：面向自动驾驶的高效三维物体检测框架

GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving

Despite the great success of two-stage detectors, single-stage detector is
still a more elegant and efficient way, yet suffers from the two well-known
disharmonies during training, i.e. the huge difference in quantity between
positive and negative examples as well as between easy and hard examples. In
this work, we first point out that the essential effect of the two disharmonies
can be summarized in term of the gradient. Further, we propose a novel gradient
harmonizing mechanism (GHM) to be a hedging for the disharmonies. The
philosophy behind GHM can be easily embedded into both classification loss
function like cross-entropy (CE) and regression loss function like smooth-$L_1$
($SL_1$) loss. To this end, two novel loss functions called GHM-C and GHM-R are
designed to balancing the gradient flow for anchor classification and bounding
box refinement, respectively. Ablation study on MS COCO demonstrates that
without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring
substantial improvement for single-stage detector. Without any whistles and
bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the
state-of-the-art method, Focal Loss (FL) + $SL_1$, by 0.8.

本论文提出了一种新的梯度协调机制 (GHM)，可有效解决单级检测器在训练过程中积极和消极样例以及简单和困难样例数量巨大差异所带来的问题，进而设计了两种新的损失函数  GHM-C 和 GHM-R 用于调和梯度流以提高检测器性能。实验证明，GHM-C 和 GHM-R 在 MS COCO 的表现优于当前最先进的方法，该模型在 COCO 测试集上实现了 41.6 mAP。