Our project proposes an end-to-end 3D face alignment and reconstruction
network. The backbone of our model is built by Bottle-Neck structure via
Depth-wise Separable Convolution. We integrate Coordinate Attention mechanism
and Spatial Group-wise Enhancement to extract more representative features. For
more stable training process and better convergence, we jointly use Wing loss
and the Weighted Parameter Distance Cost to learn parameters for 3D Morphable
model and 3D vertices. Our proposed model outperforms all baseline models both
quantitatively and qualitatively.

提出了一个端到端的 3D 人脸对齐和重建网络，使用了瓶颈结构、坐标注意机制和 Wing 损失来提取更具代表性的特征，该模型在定量和定性方面均优于基准模型。

CSANet：稳健的 3D 人脸对齐和重建的通道空间注意力网络

CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment  and Reconstruction

The swift and precise detection of vehicles plays a significant role in
intelligent transportation systems. Current vehicle detection algorithms
encounter challenges of high computational complexity, low detection rate, and
limited feasibility on mobile devices. To address these issues, this paper
proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You
Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled
to 0.5 and the standard convolution of the backbone network is replaced with
Ghost convolution to achieve a lighter network and improve the detection speed;
then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN)
is embedded into the neck network to enhance feature extraction capability of
the algorithm and enriches semantic information; and a Ghost Decouoled Head
(GDH) is employed for accurate prediction of vehicle location and species;
finally, a coordinate attention mechanism is introduced into the output layer
to suppress environmental interference. The WIoU loss function is employed to
further enhance the detection accuracy. Ablation experiments results on the
PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original
YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3%
reduction in the number of parameters, 35.1% reduction in model weights, 1.1%
higher mean average precision (mAP), the detection speed is higher 27FPS
compared with the original algorithm. Ghost-YOLOv7 was also compared on KITTI
and BIT-vehicle datasets as well, and the results show that this algorithm has
the overall best performance.

本文提出一种基于 Ghost-YOLOv7 的轻量级车辆检测算法，该算法的宽度为 0.5，而且使用 Ghost convolution 替换了骨干网络的标准卷积以实现轻量级化、提高检测速度，进而增强了特征提取能力、丰富了语义信息并提高了检测准确度。同时，引入坐标注意力机制来抑制环境干扰。该算法的实验表明，相较于原有算法，检测速度提高 27FPS，能够有效降低计算复杂度和参数数量，且在多项指标上表现更好，可用于智能交通系统。