The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

在这份技术报告中，我们将解决方案介绍给以人为中心的时空视频接地任务。我们提出了一个名为stvgformer的简洁有效框架，该框架将时空视觉语言依赖性与静态分支和动态分支建模。静态分支在单个帧中执行交叉模式的理解，并根据框架内视觉提示（如对象出现）学会在空间上定位目标对象。动态分支在多个帧上执行交叉模式理解。它学会了根据动作（如动作）的动态视觉提示来预测目标力矩的开始和结束时间。静态分支和动态分支均设计为跨模式变压器。我们进一步设计了一种新型的静态动力相互作用块，以使静态和动态分支相互传递有用和互补信息，这被证明可以有效地改善对硬病例的预测。我们提出的方法获得了39.6％的VIOU，并在第四人中挑战中获得了HC-STVG曲目的第一名。

现有的光流估计器通常采用通常用于图像分类的网络体系结构作为提取人均功能的编码器。但是，由于任务之间的自然差异，用于图像分类的架构可能是最佳的流量估计。为了解决此问题，我们建议一种名为Falownas的神经体系结构搜索方法，以自动找到用于流估计任务的更好的编码器体系结构。我们首先设计一个合适的搜索空间，包括各种卷积运算符，并构建一个体重共享的超级网络，以有效评估候选体系结构。然后，为了更好地训练超级网络，我们提出了特征对齐蒸馏，该蒸馏利用训练有素的流量估计器来指导超级网络的训练。最后，利用资源约束的进化算法找到最佳体系结构（即子网络）。实验结果表明，从超级网络继承的权重的发现的结构达到了4.67 \％f1-able kitti上的误差，这是RAFT基线的8.4 \％降低，超过了先进的手工制作的型号GMA和AGFlow，同时降低模型的复杂性和延迟。源代码和训练有素的模型将在https://github.com/vdigpku/flownas中发布。

超声检查中的乳腺病变检测对于乳腺癌诊断至关重要。现有方法主要依赖于单独的2D超声图像或组合未标记的视频和标记为2D图像以训练模型以进行乳腺病变检测。在本文中，我们首先收集并注释一个超声视频数据集（188个视频），以进行乳腺病变检测。此外，我们通过汇总视频级别的病变分类功能和剪辑级的时间功能来解决超声视频中乳房病变检测的解决剪辑级和视频级特征聚合网络（CVA-NET）。剪辑级的时间功能特征编码有序视频框架的本地时间信息和洗牌视频帧的全局时间信息。在我们的CVA-NET中，设计了一个Inter-Video融合模块，以融合原始视频框架的本地功能以及从洗牌视频帧中的全局功能，并设计了一个内部视频融合模块，以学习相邻视频框架之间的时间信息。此外，我们学习视频水平功能，以将原始视频的乳房病变分类为良性或恶性病变，以进一步增强超声视频中最终的乳房病变检测性能。我们注释数据集的实验结果表明，我们的CVA-NET显然优于最先进的方法。相应的代码和数据集可在\ url {https://github.com/jhl-det/cva-net}上公开获得。

图形神经网络（GNN）已被密切应用于各种基于图的应用程序。尽管他们成功了，但手动设计行为良好的GNN需要巨大的人类专业知识。因此，发现潜在的最佳数据特异性GNN体系结构效率低下。本文提出了DFG-NAS，这是一种新的神经体系结构搜索（NAS）方法，可自动搜索非常深入且灵活的GNN体系结构。与大多数专注于微构造的方法不同，DFG-NAS突出了另一个设计级别：搜索有关原子传播的宏观构造（\ TextBf {\ Textbf {\ Texttt {p}}}）和转换（\ texttt {\ textttt {\ texttt {\ texttt {\ texttt { T}}）的操作被整合并组织到GNN中。为此，DFG-NAS为\ textbf {\ texttt {p-t}}}的排列和组合提出了一个新颖的搜索空间，该搜索空间是基于消息传播的散布，定义了四个自定义设计的宏观架构突变，并采用了进化性algorithm to to the Evolutionary algorithm进行有效的搜索。关于四个节点分类任务的实证研究表明，DFG-NAS优于最先进的手动设计和GNN的NAS方法。

Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to leverage the feature from the image space. However, people discovered that this underlying assumption makes the current fusion framework infeasible to produce any prediction when there is a LiDAR malfunction, regardless of minor or major. This fundamentally limits the deployment capability to realistic autonomous driving scenarios. In contrast, we propose a surprisingly simple yet novel fusion framework, dubbed BEVFusion, whose camera stream does not depend on the input of LiDAR data, thus addressing the downside of previous methods. We empirically show that our framework surpasses the state-of-the-art methods under the normal training settings. Under the robustness training settings that simulate various LiDAR malfunctions, our framework significantly surpasses the state-of-the-art methods by 15.7% to 28.9% mAP. To the best of our knowledge, we are the first to handle realistic LiDAR malfunction and can be deployed to realistic scenarios without any post-processing procedure. The code is available at https://github.com/ADLab-AutoDrive/BEVFusion.

在计算机视觉社区中，从自然场景图像（NSI-SOD）的突出对象检测中取得了巨大进展;相比之下，光学遥感图像（RSI-SOD）中的突出物体检测仍然是一个具有挑战性的新兴主题。光学RSI的独特特性，如尺度，照明和成像方向，在NSI-SOD和RSI-SOD之间带来显着差异。在本文中，我们提出了一种新的多内容互补网络（MCCNet）来探讨RSI-SOD的多个内容的互补性。具体地，MCCNet基于常规编码器解码器架构，并包含一个名为多内容互补模块（MCCM）的新型密钥组件，其桥接编码器和解码器。在MCCM中，我们考虑多种类型的功能对RSI-SOD至关重要的功能，包括前景特征，边缘功能，后台功能和全局图像级别功能，并利用它们之间的内容互补性来突出显示RSI中各种刻度的突出区域通过注意机制的特点。此外，我们全面引入训练阶段的像素级，地图级和公制感知损失。在两个流行的数据集上进行广泛的实验表明，所提出的MCCNet优于23个最先进的方法，包括NSI-SOD和RSI-SOD方法。我们方法的代码和结果可在https://github.com/mathlee/mccnet上获得。

Deepfakes的恶意应用（即，从面部图像产生目标面部属性或整个面部的技术）对个人的声誉和安全构成了巨大的威胁。为了减轻这些威胁，最近的研究已经提出了对抗DeepFake模型的对抗水印，导致它们产生扭曲的输出。尽管结果令人印象深刻，但这些对抗水印具有低的图像水平和模型级可转移性，这意味着它们可以仅保护一个特定的DeepFake模型的一个面部图像。为了解决这些问题，我们提出了一种新的解决方案，可以产生跨模型通用对抗水印（CMUA-Watermark），保护来自多个DeepFake模型的大量面部图像。具体而言，我们首先提出跨模型通用攻击管道，迭代地攻击多个DeepFake模型。然后，我们设计了一种双层扰动融合策略，以减轻不同面部图像和模型产生的对抗水印之间的冲突。此外，我们通过启发式方法解决了跨模型优化的关键问题，以自动找到不同型号的合适的攻击步骤尺寸，进一步削弱了模型级冲突。最后，我们介绍了一种更合理和全面的评估方法来完全测试所提出的方法并将其与现有的方法进行比较。广泛的实验结果表明，所提出的CMUA-Watermark可以有效地扭曲由多个DeepFake模型产生的假面部图像，同时实现比现有方法更好的性能。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".