Generic object detection is a category-independent task that relies on
accurate modeling of objectness. Most relevant CNN-based models of objectness
utilize loss functions (e.g., binary cross entropy) that focus on the
single-response, i.e., the loss response of a single pixel. Inspired by the
human visual system, which first discerns the boundaries of ambiguous regions
(i.e., hard regions) before delving into the semantic meaning, we propose a
novel loss function, Spatial Coherence Loss (SCLoss), that uses the mutual
response between adjacent pixels to suppress or emphasize the single-response
of pixels. We demonstrate that the proposed SCLoss can gradually learn the hard
regions by detecting and emphasizing their boundaries. Through comprehensive
experiments, we demonstrate that replacing popular loss functions with SCLoss
can improve the performance of current state-of-the-art (SOTA) salient or
camouflaged object detection (SOD or COD) models. We also demonstrate that
combining SCLoss with other loss functions can further improve performance and
result in the SOTA outcomes for different applications. Finally, as a
demonstrative example of the potential uses for other related tasks, we show an
application of SCLoss for semantic segmentation.

通用物体检测是一项与类别无关的任务，它依赖于对象性的准确建模。我们提出了一种新的损失函数，即空间一致性损失（SCLoss），它利用相邻像素之间的相互响应来抑制或强调像素的单一响应，通过检测和强调边界逐渐学习困难区域。通过全面的实验证明，使用 SCLoss 替代流行的损失函数可以提高当前最先进的显著或伪装物体检测（SOD 或 COD）模型的性能。同时，我们还展示了将 SCLoss 与其他损失函数结合可以进一步提高性能并实现不同应用领域的最先进结果。最后，我们展示了将 SCLoss 应用于语义分割等其他相关任务的潜在用途的示例。

显著与伪装目标检测及其扩展的空间相干性损失

Spatial Coherence Loss for Salient and Camouflaged Object Detection and  Beyond

In this paper, we present Generic Object Detection (GenOD), one of the
largest object detection systems deployed to a web-scale general visual search
engine that can detect over 900 categories for all Microsoft Bing Visual Search
queries in near real-time. It acts as a fundamental visual query understanding
service that provides object-centric information and shows gains in multiple
production scenarios, improving upon domain-specific models. We discuss the
challenges of collecting data, training, deploying and updating such a
large-scale object detection model with multiple dependencies. We discuss a
data collection pipeline that reduces per-bounding box labeling cost by 81.5%
and latency by 61.2% while improving on annotation quality. We show that GenOD
can improve weighted average precision by over 20% compared to multiple
domain-specific models. We also improve the model update agility by nearly 2
times with the proposed disjoint detector training compared to joint
fine-tuning. Finally we demonstrate how GenOD benefits visual search
applications by significantly improving object-level search relevance by 54.9%
and user engagement by 59.9%.

本文介绍 Generic Object Detection（GenOD）系统，是一个可以在微软 Bing Visual Search 的所有查询中实时检测超过 900 个类别的最大的物体检测系统之一，可提供以物体为中心的信息，并在多种生产场景中表现出色。作者讨论了收集数据、训练、部署和更新此类大规模物体检测模型所面临的挑战。我们展示了 GenOD 相对于多个领域特定模型可以提高 20% 以上的加权平均精度，并通过所提出的不相交探测器训练来将模型更新敏捷度提高近 2 倍。最后，我们展示了 GenOD 如何通过显著提高搜索相关性和用户参与度来改善视觉搜索应用。

Microsoft Bing 上的大规模通用目标检测

Web-Scale Generic Object Detection at Microsoft Bing

Face detection has received intensive attention in recent years. Many works
present lots of special methods for face detection from different perspectives
like model architecture, data augmentation, label assignment and etc., which
make the overall algorithm and system become more and more complex. In this
paper, we point out that \textbf{there is no gap between face detection and
generic object detection}. Then we provide a strong but simple baseline method
to deal with face detection named TinaFace. We use ResNet-50 \cite{he2016deep}
as backbone, and all modules and techniques in TinaFace are constructed on
existing modules, easily implemented and based on generic object detection. On
the hard test set of the most popular and challenging face detection benchmark
WIDER FACE \cite{yang2016wider}, with single-model and single-scale, our
TinaFace achieves 92.1\% average precision (AP), which exceeds most of the
recent face detectors with larger backbone. And after using test time
augmentation (TTA), our TinaFace outperforms the current state-of-the-art
method and achieves 92.4\% AP. The code will be available at
https://github.com/Media-Smart/vedadet.

该研究使用现有的模块和通用物体检测模型构建 TinaFace，实现人脸检测。TinaFace 在 WIDER FACE 挑战赛上实现了 92.1% 的精度。使用测试时增强后，TinaFace 的结果超过了当前最先进的方法，达到 92.4% 的平均精度（AP）。