Lane detection plays a crucial role in autonomous driving by providing vital
data to ensure safe navigation. Modern algorithms rely on anchor-based
detectors, which are then followed by a label assignment process to categorize
training detections as positive or negative instances based on learned
geometric attributes. The current methods, however, have limitations and might
not be optimal since they rely on predefined classical cost functions that are
based on a low-dimensional model. Our research introduces MatchNet, a deep
learning sub-module-based approach aimed at enhancing the label assignment
process. Integrated into a state-of-the-art lane detection network like the
Cross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replaces
the conventional label assignment process with a sub-module network. This
integration results in significant improvements in scenarios involving curved
lanes, with remarkable improvement across all backbones of +2.8% for ResNet34,
+2.3% for ResNet101, and +2.96% for DLA34. In addition, it maintains or even
improves comparable results in other sections. Our method boosts the confidence
level in lane detection, allowing an increase in the confidence threshold. The
code will be available soon: this https URL

我们的研究引入了 MatchNet，一个基于深度学习子模块的方法，用于增强标签分配过程，通过将其整合到最先进的车道检测网络 CLRNet 中，我们取代了传统的标签分配过程。这种整合在涉及曲线车道的场景中取得了显著的改进，在 ResNet34，ResNet101 和 DLA34 的所有主干上分别提高了 + 2.8％，+2.3％和 + 2.96％。此外，在其他部分保持或甚至改善了可比的结果，我们的方法提高了车道检测的置信水平，允许提高置信阈值。

CLRmatchNet: 借助深度匹配过程增强曲线车道检测

CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process

Human pose estimation (i.e., locating the body parts / joints of a person) is
a fundamental problem in human-computer interaction and multimedia
applications. Significant progress has been made based on the development of
depth sensors, i.e., accessible human pose prediction from still depth images
[32]. However, most of the existing approaches to this problem involve several
components/models that are independently designed and optimized, leading to
suboptimal performances. In this paper, we propose a novel inference-embedded
multi-task learning framework for predicting human pose from still depth
images, which is implemented with a deep architecture of neural networks.
Specifically, we handle two cascaded tasks: i) generating the heat (confidence)
maps of body parts via a fully convolutional network (FCN); ii) seeking the
optimal configuration of body parts based on the detected body part proposals
via an inference built-in MatchNet [10], which measures the appearance and
geometric kinematic compatibility of body parts and embodies the dynamic
programming inference as an extra network layer. These two tasks are jointly
optimized. Our extensive experiments show that the proposed deep model
significantly improves the accuracy of human pose estimation over other several
state-of-the-art methods or SDKs. We also release a large-scale dataset for
comparison, which includes 100K depth images under challenging scenarios.

本文提出一种多任务学习框架，采用深度神经网络进行实现，用于从静止深度图像中预测人体姿态，并使用 MatchNet 协助推断。在大规模数据集上的实验证明该框架可以显著提高人体姿态估计的准确度。