The task of lane detection has garnered considerable attention in the field
of autonomous driving due to its complexity. Lanes can present difficulties for
detection, as they can be narrow, fragmented, and often obscured by heavy
traffic. However, it has been observed that the lanes have a geometrical
structure that resembles a straight line, leading to improved lane detection
results when utilizing this characteristic. To address this challenge, we
propose a hierarchical Deep Hough Transform (DHT) approach that combines all
lane features in an image into the Hough parameter space. Additionally, we
refine the point selection method and incorporate a Dynamic Convolution Module
to effectively differentiate between lanes in the original image. Our network
architecture comprises a backbone network, either a ResNet or Pyramid Vision
Transformer, a Feature Pyramid Network as the neck to extract multi-scale
features, and a hierarchical DHT-based feature aggregation head to accurately
segment each lane. By utilizing the lane features in the Hough parameter space,
the network learns dynamic convolution kernel parameters corresponding to each
lane, allowing the Dynamic Convolution Module to effectively differentiate
between lane features. Subsequently, the lane features are fed into the feature
decoder, which predicts the final position of the lane. Our proposed network
structure demonstrates improved performance in detecting heavily occluded or
worn lane images, as evidenced by our extensive experimental results, which
show that our method outperforms or is on par with state-of-the-art techniques.

我们提出了一种基于深度 Hough 变换和动态卷积模块的网络架构，用于检测道路车道，通过在 Hough 参数空间中将车道特征结合起来并利用动态卷积模块有效区分车道特征，实验结果表明我们的方法在检测重度遮挡或磨损车道图像方面表现出了优异的性能。

HoughLaneNet：深度霍夫变换与动态卷积的车道检测

HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic  Convolution

The key challenge for few-shot semantic segmentation (FSS) is how to tailor a
desirable interaction among support and query features and/or their prototypes,
under the episodic training scenario. Most existing FSS methods implement such
support-query interactions by solely leveraging plain operations - e.g., cosine
similarity and feature concatenation - for segmenting the query objects.
However, these interaction approaches usually cannot well capture the intrinsic
object details in the query images that are widely encountered in FSS, e.g., if
the query object to be segmented has holes and slots, inaccurate segmentation
almost always happens. To this end, we propose a dynamic prototype convolution
network (DPCN) to fully capture the aforementioned intrinsic details for
accurate FSS. Specifically, in DPCN, a dynamic convolution module (DCM) is
firstly proposed to generate dynamic kernels from support foreground, then
information interaction is achieved by convolution operations over query
features using these kernels. Moreover, we equip DPCN with a support activation
module (SAM) and a feature filtering module (FFM) to generate pseudo mask and
filter out background information for the query images, respectively. SAM and
FFM together can mine enriched context information from the query features. Our
DPCN is also flexible and efficient under the k-shot FSS setting. Extensive
experiments on PASCAL-5i and COCO-20i show that DPCN yields superior
performances under both 1-shot and 5-shot settings.

针对 few-shot 语义分割的挑战，该论文提出了一种基于动态原型卷积网络 (DPCN) 的解决方案，其中动态卷积模块 (DCM) 从支持前景生成动态内核，而支持激活模块 (SAM) 和特征过滤模块 (FFM) 则一起为查询图像提供丰富的背景信息，实现了更准确的 few-shot 语义分割。DPCN 在 1-shot 和 5-shot 设置下均表现出优越性能。