Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides algorithmic crowdsourcing into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved the new record 65.4 mAP on COCO test-dev. The code will be released at https://github.com/OpenGVLab/InternImage.

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the conventional estimated time of arrival (ETA), it is more challenging with the following aspects: 1) Inductive inference. Models are required to predict ETA for orders with unseen retailers and addresses; 2) High-order interaction of order semantic information. Apart from the spatio-temporal features, the estimated time also varies greatly with other factors, such as the packaging efficiency of retailers, as well as the high-order interaction of these factors. In this paper, we propose an inductive graph transformer (IGT) that leverages raw feature information and structural graph data to estimate package delivery time. Different from previous graph transformer architectures, IGT adopts a decoupled pipeline and trains transformer as a regression function that can capture the multiplex information from both raw feature and dense embeddings encoded by a graph neural network (GNN). In addition, we further simplify the GNN structure by removing its non-linear activation and the learnable linear transformation matrix. The reduced parameter search space and linear information propagation in the simplified GNN enable the IGT to be applied in large-scale industrial scenarios. Experiments on real-world logistics datasets show that our proposed model can significantly outperform the state-of-the-art methods on estimation of delivery time. The source code is available at: https://github.com/enoche/IGT-WSDM23.

Bimanual activities like coffee stirring, which require coordination of dual arms, are common in daily life and intractable to learn by robots. Adopting reinforcement learning to learn these tasks is a promising topic since it enables the robot to explore how dual arms coordinate together to accomplish the same task. However, this field has two main challenges: coordination mechanism and long-horizon task decomposition. Therefore, we propose the Mixline method to learn sub-tasks separately via the online algorithm and then compose them together based on the generated data through the offline algorithm. We constructed a learning environment based on the GPU-accelerated Isaac Gym. In our work, the bimanual robot successfully learned to grasp, hold and lift the spoon and cup, insert them together and stir the coffee. The proposed method has the potential to be extended to other long-horizon bimanual tasks.

在鸟眼中学习强大的表现（BEV），以进行感知任务，这是趋势和吸引行业和学术界的广泛关注。大多数自动驾驶算法的常规方法在正面或透视视图中执行检测，细分，跟踪等。随着传感器配置变得越来越复杂，从不同的传感器中集成了多源信息，并在统一视图中代表功能至关重要。 BEV感知继承了几个优势，因为代表BEV中的周围场景是直观和融合友好的。对于BEV中的代表对象，对于随后的模块，如计划和/或控制是最可取的。 BEV感知的核心问题在于（a）如何通过从透视视图到BEV来通过视图转换来重建丢失的3D信息； （b）如何在BEV网格中获取地面真理注释； （c）如何制定管道以合并来自不同来源和视图的特征； （d）如何适应和概括算法作为传感器配置在不同情况下各不相同。在这项调查中，我们回顾了有关BEV感知的最新工作，并对不同解决方案进行了深入的分析。此外，还描述了该行业的BEV方法的几种系统设计。此外，我们推出了一套完整的实用指南，以提高BEV感知任务的性能，包括相机，激光雷达和融合输入。最后，我们指出了该领域的未来研究指示。我们希望该报告能阐明社区，并鼓励对BEV感知的更多研究。我们保留一个活跃的存储库来收集最新的工作，并在https://github.com/openperceptionx/bevperception-survey-recipe上提供一包技巧的工具箱。

可靠的皮肤癌诊断模型在早期筛查和医疗干预中起着至关重要的作用。流行的计算机辅助皮肤癌分类系统采用深度学习方法。然而，最近的研究揭示了它们对对抗攻击的极端脆弱性 - 通常无法察觉地扰动，可显着降低皮肤癌诊断模型的性能。为了减轻这些威胁，这项工作通过在皮肤癌图像中进行反向对抗性扰动提出了一个简单，有效和资源有效的防御框架。具体而言，首先建立了多尺度图像金字塔，以更好地保留医学成像域中的判别结构。为了中和对抗性效应，通过注射各向同性高斯噪声将不同尺度的皮肤图像逐渐扩散，以将对抗性示例移至干净的图像歧管。至关重要的是，为了进一步逆转对抗性的噪音并抑制了冗余的注射噪声，精心设计了一种新型的多尺度降级机制，可以从相邻尺度汇总图像信息。我们评估了方法对ISIC 2019的防御效果，这是最大的皮肤癌多类分类数据集。实验结果表明，所提出的方法可以成功地逆转不同攻击的对抗扰动，并且在捍卫皮肤癌诊断模型中的某些最新方法明显优于某些最先进的方法。

每个例子梯度剪辑是一个关键算法步骤，可实现对深度学习模型的实用差异私有（DP）培训。但是，剪辑规范$ r $的选择对于在DP下实现高精度至关重要。我们提出了一个易于使用的替代品，称为Autoclipping，它消除了任何DP优化器（包括DP-SGD，DP-ADAM，DP-LAMB等）调整$ R $的需求。自动变体与现有的DP优化器一样私有和计算效率，但不需要DP特定的超参数，因此使DP培训与标准的非私人培训一样适合。我们在非凸vex设置中对自动DP-SGD进行了严格的融合分析，这表明它具有与标准SGD相匹配的渐近收敛速率。我们还展示了各种语言和视觉任务，这些任务自动剪辑优于或匹配最新的，并且可以轻松使用对现有代码库的最小更改。

3D视觉感知任务，包括基于多相机图像的3D检测和MAP分割，对于自主驾驶系统至关重要。在这项工作中，我们提出了一个称为BeVformer的新框架，该框架以时空变压器学习统一的BEV表示，以支持多个自主驾驶感知任务。简而言之，Bevormer通过通过预定义的网格形BEV查询与空间和时间空间进行交互来利用空间和时间信息。为了汇总空间信息，我们设计了空间交叉注意，每个BEV查询都从相机视图中从感兴趣的区域提取了空间特征。对于时间信息，我们提出暂时的自我注意力，以将历史bev信息偶尔融合。我们的方法在Nuscenes \ texttt {test} set上，以NDS度量为单位达到了新的最新56.9 \％，该设置比以前的最佳艺术高9.0分，并且与基于LIDAR的盆地的性能相当。我们进一步表明，BeVormer明显提高了速度估计的准确性和在低可见性条件下对象的回忆。该代码可在\ url {https://github.com/zhiqi-li/bevformer}中获得。

Panoptic semonation涉及联合语义分割和实例分割的组合，其中图像内容分为两种类型：事物和东西。我们展示了Panoptic SegFormer，是与变压器的Panoptic Semonation的一般框架。它包含三个创新组件：高效的深度监督掩模解码器，查询解耦策略以及改进的后处理方法。我们还使用可变形的DETR来有效地处理多尺度功能，这是一种快速高效的DETR版本。具体而言，我们以层式方式监督掩模解码器中的注意模块。这种深度监督策略让注意模块快速关注有意义的语义区域。与可变形的DETR相比，它可以提高性能并将所需培训纪元的数量减少一半。我们的查询解耦策略对查询集的职责解耦并避免了事物和东西之间的相互干扰。此外，我们的后处理策略通过联合考虑分类和分割质量来解决突出的面具重叠而没有额外成本的情况。我们的方法会在基线DETR模型上增加6.2 \％PQ。 Panoptic SegFormer通过56.2 \％PQ实现最先进的结果。它还显示出对现有方法的更强大的零射鲁布利。代码释放\ url {https://github.com/zhiqi-li/panoptic-segformer}。

星际争霸II（SC2）是一个实时策略游戏，其中玩家生产和控制多个单位来对抗对手的单位。由于其困难，如巨大的国家空间，各种动作空间，长时间地平线和不完美的信息，SC2一直是加固学习的研究热点。最近，已经提出了一个称为阿尔巴斯塔（AS）的代理人，这表明了良好的性能，抵御人类球员的高胜率为99.8％。我们根据AS的纸张和伪代码实现了称为Mini-AlphaStar（MAS）的迷你缩放版本。AS和MAS之间的差异是，我们将与较小的培训培训的超参数替换为较小的参数。MAS的代码都是开放的（https://github.com/liuruoze/minia-alphastar），用于将来的研究。