Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

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.

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. To the best of our knowledge, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images by ignoring the geoscience related tasks. This paper aims to repair the occluded regions for a better geoscience task performance with the advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we use a MaskMix based data augmentation method to exploit more information from limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method.

Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges. It is well known that pairwise similarities between images are sensitive to the noise in feature representations, leading to unreliable graph structures. We address this problem from the viewpoint of statistical tests. By viewing the feature vector of each node as an independent sample, the decision of whether creating an edge between two nodes based on their similarity in feature representation can be thought as a ${\it single}$ statistical test. To improve the robustness in the decision of creating an edge, multiple samples are drawn and integrated by ${\it multiple}$ statistical tests to generate a more reliable similarity measure, consequentially more reliable graph structure. The corresponding elegant matrix form named $\mathcal{B}\textbf{-Attention}$ is designed for efficiency. The effectiveness of multiple tests for graph structure learning is verified both theoretically and empirically on multiple clustering and ReID benchmark datasets. Source codes are available at https://github.com/Thomas-wyh/B-Attention.

在无监督的域适应性（UDA）中，直接从源到目标域的适应通常会遭受明显的差异，并导致对齐不足。因此，许多UDA的作品试图通过各种中间空间逐渐和轻柔地消失域间隙，这些空间被称为域桥接（DB）。但是，对于诸如域自适应语义分割（DASS）之类的密集预测任务，现有的解决方案主要依赖于粗糙的样式转移以及如何优雅地桥接域的优雅桥梁。在这项工作中，我们诉诸于数据混合以建立用于DASS的经过经过经过经过讨论的域桥接（DDB），通过该域的源和目标域的联合分布与中间空间中的每个分布进行对齐并与每个分布。 DDB的核心是双路径域桥接步骤，用于使用粗糙和精细的数据混合技术生成两个中间域，以及一个跨路径知识蒸馏步骤，用于对两个互补模型进行对生成的中间样品进行培训的互补模型作为“老师”以多教老师的蒸馏方式发展出色的“学生”。这两个优化步骤以交替的方式工作，并相互加强以具有强大的适应能力引起DDB。对具有不同设置的自适应分割任务进行的广泛实验表明，我们的DDB显着优于最先进的方法。代码可从https://github.com/xiaoachen98/ddb.git获得。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

由于深度神经网络的开发，尤其是对于最近开发的无监督的JND代模型，对公正的显着差异（JND）建模做出了重大改进。但是，他们有一个主要的缺点，即在现实世界信号域而不是在人脑中的感知结构域中评估了生成的JND。当在这两个域中评估JND时，存在明显的差异，因为在现实世界中的视觉信号在通过人类视觉系统（HVS）传递到大脑之前已编码。因此，我们提出了一个受HVS启发的信号降解网络进行JND估计。为了实现这一目标，我们仔细分析了JND主观观察中的HVS感知过程，以获得相关的见解，然后设计受HVS启发的信号降解（HVS-SD）网络，以表示HVS中的信号降解。一方面，知识渊博的HVS-SD使我们能够评估感知域中的JND。另一方面，它提供了更准确的先验信息，以更好地指导JND生成。此外，考虑到合理的JND不应导致视觉注意力转移的要求，提出了视觉注意力丧失以控制JND的生成。实验结果表明，所提出的方法实现了SOTA性能，以准确估计HVS的冗余性。源代码将在https://github.com/jianjin008/hvs-sd-jnd上找到。

从单个RGB图像中估算3D相互作用的手姿势对于理解人类行为至关重要。与大多数直接预测两只相互作用手的3D姿势的先前作品不同，我们建议分解具有挑战性的相互作用姿势估计任务并分别估算每只手的姿势。这样，就可以直接利用单手姿势估计系统的最新研究进度。然而，由于（1）严重的手部阻塞和（2）手的歧义性，手动姿势估计在相互作用的情况下非常具有挑战性。为了应对这两个挑战，我们提出了一种新型的手部划分和去除（HDR）框架，以执行手部斜切和脱离分散术的去除。我们还提出了第一个称为Amodal intredhand数据集（AIH）的大规模合成Amodal手数据集，以促进模型培训并促进相关研究的开发。实验表明，所提出的方法显着优于先前的最新相互作用姿势估计方法。代码和数据可在https://github.com/menghao666/hdr上找到。

在各种设备上部署深度学习模型已成为一个重要的话题。硬件专业化的浪潮为多维张量计算带来了一套多样化的加速度原始图。这些新的加速原始基原料以及新兴的机器学习模型带来了巨大的工程挑战。在本文中，我们提出了Tensorir，这是一种编译器抽象，用于通过这些张量计算原始素优化程序。Tensorir概括了现有机器学习编译器中使用的循环巢表示，以将张量计算作为一流的公民。最后，我们在抽象之上构建了一个端到端框架，以自动优化给定的张量计算原始图的深度学习模型。实验结果表明，Tensorir编译会自动使用给定硬件后端的张量计算原始图，并提供与跨平台的最新手工精制系统竞争性能的性能。

服务机器人安全有礼貌的机器人需要坚强地跟踪周围人，尤其是对于旅游指南机器人（TGR）。但是，由于以下原因，现有的多对象跟踪（MOT）或多人跟踪（MPT）方法不适用于TGR：1。缺乏相关的大型数据集；2.缺少适用的指标来评估跟踪器。在这项工作中，我们针对TGR的视觉感知任务，并介绍TGRDB数据集，TGRDB数据集是一种新颖的大型多人跟踪数据集，其中包含大约5.6小时的带注释视频和超过450个长期轨迹。此外，我们提出了一个更适合使用数据集评估跟踪器的指标。作为我们工作的一部分，我们提出了TGRMPT，这是一种新型的MPT系统，它结合了头部肩膀和全身的信息，并实现了最先进的性能。我们已经在https://github.com/wenwenzju/tgrmpt中发布了代码和数据集。