Sparse algorithms offer great flexibility for multi-view temporal perception
tasks. In this paper, we present an enhanced version of Sparse4D, in which we
improve the temporal fusion module by implementing a recursive form of
multi-frame feature sampling. By effectively decoupling image features and
structured anchor features, Sparse4D enables a highly efficient transformation
of temporal features, thereby facilitating temporal fusion solely through the
frame-by-frame transmission of sparse features. The recurrent temporal fusion
approach provides two main benefits. Firstly, it reduces the computational
complexity of temporal fusion from $O(T)$ to $O(1)$, resulting in significant
improvements in inference speed and memory usage. Secondly, it enables the
fusion of long-term information, leading to more pronounced performance
improvements due to temporal fusion. Our proposed approach, Sparse4Dv2, further
enhances the performance of the sparse perception algorithm and achieves
state-of-the-art results on the nuScenes 3D detection benchmark. Code will be
available at https://github.com/linxuewu/Sparse4D.

本文提出了 Sparse4Dv2 算法，通过在时间融合模块中实现一种递归形式的多帧特征采样来提高性能，并达到了 nuScenes 3D 检测基准的最佳结果。

Sparse4D v2：带稀疏模型的循环时间融合

Sparse4D v2: Recurrent Temporal Fusion with Sparse Model

Graph matching is a challenging problem with very important applications in a
wide range of fields, from image and video analysis to biological and
biomedical problems. We propose a robust graph matching algorithm inspired in
sparsity-related techniques. We cast the problem, resembling group or
collaborative sparsity formulations, as a non-smooth convex optimization
problem that can be efficiently solved using augmented Lagrangian techniques.
The method can deal with weighted or unweighted graphs, as well as multimodal
data, where different graphs represent different types of data. The proposed
approach is also naturally integrated with collaborative graph inference
techniques, solving general network inference problems where the observed
variables, possibly coming from different modalities, are not in
correspondence. The algorithm is tested and compared with state-of-the-art
graph matching techniques in both synthetic and real graphs. We also present
results on multimodal graphs and applications to collaborative inference of
brain connectivity from alignment-free functional magnetic resonance imaging
(fMRI) data. The code is publicly available.

研究提出了一种基于稀疏技术的鲁棒图匹配算法，可处理加权或非加权图，也适用于不同类型数据的多模态图，并结合合作式图推理技术解决不同模态数据的一般网络推理问题，并以脑功能磁共振成像数据为例进行了应用与验证。