The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

从搜索效率中受益，可区分的神经体系结构搜索（NAS）已发展为自动设计竞争性深神经网络（DNNS）的最主要替代品。我们注意到，必须在现实世界中严格的性能限制下执行DNN，例如，自动驾驶汽车的运行时间延迟。但是，要获得符合给定性能限制的体系结构，先前的硬件可区分的NAS方法必须重复多次搜索运行，以通过反复试验和错误手动调整超参数，因此总设计成本会成比例地增加。为了解决这个问题，我们引入了一个轻巧的硬件可区分的NAS框架，称为lightnas，努力找到所需的架构，通过一次性搜索来满足各种性能约束（即，\ \ suesperline {\ textIt {您只搜索一次}}）） 。进行了广泛的实验，以显示LINDNA的优越性，而不是先前的最新方法。

基于范围视图的LIDAR分割方法由于其直接继承了有效的2D CNN体系结构，因此对实际应用具有吸引力。在文献中，大多数基于范围的方法都遵循每个像素分类范式。最近，在图像分割域中，另一个范式将分割作为面具分类问题，并实现了出色的性能。这提出了一个有趣的问题：掩码分类范式是否可以使基于范围的LIDAR分割受益并获得比每个像素范式对应的更好的性能？为了回答这个问题，我们为基于范围视图的LIDAR语义和全景分段提出了一个统一的面膜分类模型MaskRange。除了新的范式外，我们还提出了一种新型的数据增强方法，以应对过度拟合，上下文依赖和班级不平衡问题。大量实验是在Semantickitti基准测试上进行的。在所有基于范围视图的方法中，我们的面具以$ 66.10 $ MIOU的语义细分和有希望的结果以$ 53.10 $ pq的pq pq in Panoptic细分，以高效的效率达到了最新的性能。我们的代码将发布。

视觉变压器在众多计算机视觉任务上表现出了巨大的成功。然而，由于计算复杂性和记忆足迹是二次的，因此其中心分量（软磁性注意力）禁止视觉变压器扩展到高分辨率图像。尽管在自然语言处理（NLP）任务中引入了线性注意以减轻类似问题，但直接将现有的线性注意力应用于视觉变压器可能不会导致令人满意的结果。我们研究了这个问题，发现与NLP任务相比，计算机视觉任务更多地关注本地信息。基于这一观察结果，我们提出了附近的关注，该关注引入了具有线性复杂性的视觉变压器的局部性偏见。具体而言，对于每个图像补丁，我们根据其相邻贴片测量的2D曼哈顿距离调整了注意力重量。在这种情况下，相邻的补丁比遥远的补丁会受到更大的关注。此外，由于我们的附近注意力要求令牌长度比特征维度大得多，以显示其效率优势，因此我们进一步提出了一个新的附近视觉变压器（VVT）结构，以减少特征维度而不脱离准确性。我们在CIFAR100，ImagEnet1k和ADE20K数据集上进行了广泛的实验，以验证我们方法的有效性。当输入分辨率增加时，与以前的基于变压器和基于卷积的网络相比，GFLOP的增长率较慢。特别是，我们的方法达到了最新的图像分类精度，其参数比以前的方法少50％。

在城市环境中面对道路选项问题时，现有的仿制学习方法遭受了低效率和泛化能力。在本文中，我们提出了一种横摆引导的仿制学习方法，以提高端到端自主驾驶范式的道路选择性能，就利用培训样本和对不断变化的环境的适应性而言。具体地，偏航信息由导航图的轨迹提供。我们的端到端架构，偏航引导模仿学习与Resnet34注意（YILRATT），集成了Resnet34主干和注意机制，以获得准确的感知。它不需要高精度地图，并且在给定由消费级GPS接收器提供的偏航信息的情况下实现完全端到端的自主驱动。通过分析注意热图，我们可以揭示决策和场景感知之间的一些因果关系，特别是故障情况是由错误的感知引起的。我们在Carla 0.9.11模拟器中收集专家体验，并改善基准科尔2017和NOCRASH。实验结果表明，伊利拉特比SOTA CILRS的成功率较高26.27％。代码，数据集，基准和实验结果可以在https://github.com/yandong024/yaw-guiding -il.git找到

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases.The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.

Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however, may significantly degrade the performance. To address this issue, we propose a novel algorithm -- robust consensus clustering that can find common ground truth among experts' opinions, which tends to be minimally affected by the bias caused by the outliers. In particular, we formalize the robust consensus clustering problem as a constraint optimization problem, and then derive an effective algorithm upon alternating direction method of multipliers (ADMM) with rigorous convergence guarantee. Our method outperforms the baselines on benchmarks. We apply the proposed method to the real-world advertising campaign segmentation and forecasting tasks using the proposed consensus clustering results based on the similarity computed via Kolmogorov-Smirnov Statistics. The accurate clustering result is helpful for building the advertiser profiles so as to perform the forecasting.

Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.