Although several image super-resolution solutions exist, they still face many
challenges. CNN-based algorithms, despite the reduction in computational
complexity, still need to improve their accuracy. While Transformer-based
algorithms have higher accuracy, their ultra-high computational complexity
makes them difficult to be accepted in practical applications. To overcome the
existing challenges, a novel super-resolution reconstruction algorithm is
proposed in this paper. The algorithm achieves a significant increase in
accuracy through a unique design while maintaining a low complexity. The core
of the algorithm lies in its cleverly designed Global-Local Information
Extraction Module and Basic Block Module. By combining global and local
information, the Global-Local Information Extraction Module aims to understand
the image content more comprehensively so as to recover the global structure
and local details in the image more accurately, which provides rich information
support for the subsequent reconstruction process. Experimental results show
that the comprehensive performance of the algorithm proposed in this paper is
optimal, providing an efficient and practical new solution in the field of
super-resolution reconstruction.

提出了一种新颖的超分辨率重建算法，通过独特的设计大幅提升了准确性，同时保持了低复杂度，该算法核心在于巧妙设计的全局 - 局部信息提取模块和基本块模块，通过结合全局和局部信息，实现了对图像内容更全面的理解，准确地恢复了图像的全局结构和局部细节，为后续的重建过程提供了丰富的信息支持。实验结果表明，提出的算法在超分辨率重建领域提供了最佳的综合性能，提供了一种高效实用的新解决方案。

基于全局 - 局部信息协同的单图像超分辨率

Single Image Super-Resolution Based on Global-Local Information Synergy

Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception.
End-to-end transformer-based algorithms, which detect and track objects
simultaneously, show great potential for the MOT task. However, most existing
methods focus on image-based tracking with a single object category. In this
paper, we propose an end-to-end transformer-based MOT algorithm (MotionTrack)
with multi-modality sensor inputs to track objects with multiple classes. Our
objective is to establish a transformer baseline for the MOT in an autonomous
driving environment. The proposed algorithm consists of a transformer-based
data association (DA) module and a transformer-based query enhancement module
to achieve MOT and Multiple Object Detection (MOD) simultaneously. The
MotionTrack and its variations achieve better results (AMOTA score at 0.55) on
the nuScenes dataset compared with other classical baseline models, such as the
AB3DMOT, the CenterTrack, and the probabilistic 3D Kalman filter. In addition,
we prove that a modified attention mechanism can be utilized for DA to
accomplish the MOT, and aggregate history features to enhance the MOD
performance.

提出了一种基于 Transformer 的多模态传感器输入的端到端多目标跟踪算法（MotionTrack），它由基于 Transformer 的数据关联（DA）模块和基于 Transformer 的查询增强模块组成，同时实现了多目标检测（MOD）。MotionTrack 及其变体在 nuScenes 数据集上获得更好的结果（AMOTA 得分为 0.55），与 AB3DMOT、CenterTrack 和概率 3D 卡尔曼滤波器等经典基线模型相比有着更好的表现。