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.

Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data are often complex and pose several unique challenges for machine learning models: 1) multiple models are needed to handle region-based data patterns that have significant spatial heterogeneity across different locations; 2) local models trained on region-specific data have limited ability to adapt to other regions that have large diversity and abnormality; 3) spatial and temporal variations entangle data complexity that requires more robust and adaptive models; 4) limited spatial-temporal data in real scenarios (e.g., crop yield data is collected only once a year) makes the problems intrinsically challenging. To bridge these gaps, we propose task-adaptive formulations and a model-agnostic meta-learning framework that ensembles regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.

临床领域中的语义文本相似性（STS）有助于提高诊断效率，并为下游数据挖掘任务产生简洁的文本。但是，鉴于诊所文本中涉及的高度领域知识，通用语言模型正确地推断出临床句子和输出相似性后的隐性医学关系仍然具有挑战性。在本文中，我们提出了一个图形增强的循环学习框架，以实现临床领域的相似性估计。该框架可以方便地在最先进的骨干语言模型上实现，并通过与基于辅助图的卷积网络（GCN）网络共同培训来利用域知识来提高其性能。我们报告了通过将生物临床BERT基线提高16.3％和27.9％，在GCN和共同训练框架中引入领域知识的成功。

为了使婴儿脑瘫（CP）的早期医疗干预，早期诊断出脑损伤至关重要。尽管一般运动评估（GMA）在早期CP检测中显示出令人鼓舞的结果，但它很费力。大多数现有作品都以视频为输入，以对GMA自动化进行烦躁的动作（FMS）分类。这些方法需要对视频进行完整的观察，并且无法本地化包含正常FMS的视频帧。因此，我们提出了一种名为WO-GMA的新颖方法，以在弱监督的在线环境中执行FMS本地化。首先将婴儿体重点作为WO-GMA的输入提取。然后，WO-GMA执行本地时空提取，然后进行两个网络分支，以生成伪夹标签和模型在线操作。凭借剪辑级伪标签，动作建模分支学会以在线方式检测FMS。具有757个不同婴儿视频的数据集上的实验结果表明，WO-GMA可以获得最新的视频级别分类和Cliplevel检测结果。此外，仅需要前20％的视频持续时间才能获得与完全观察到的分类结果，这意味着FMS诊断时间大大缩短了。代码可在以下网址获得：https：//github.com/scofiedluo/wo-gma。

机器学习开始在一系列环境应用中提供最先进的性能，例如水文流域中的流量预测。但是，由于主要的水文工艺的可变性，在实践中建立准确的大规模模型在实践中仍然具有挑战性，这是通过一组与过程相关的盆地特征捕获的。现有的盆地特征遭受了噪音和不确定性的影响，以及许多其他事情，这会对模型性能产生不利影响。为了应对上述挑战，在本文中，我们提出了一种新颖的知识引导的自学学习（KGSSL）逆框架，以从驱动程序和响应数据中提取系统特征。即使特征被损坏，这个首先的框架即使在特征被损坏的情况下也达到了强大的性能。我们表明，KGSSL为骆驼的流量建模（大型研究的流域属性和气象学）实现了最新的结果，这是一个广泛使用的水文基准数据集。具体而言，KGSSL在重建特性中最多优于其他方法16 \％。此外，我们表明KGSSL比基线方法相对强大，并且在插入KGSSL推断的特征时，基线模型的表现优于35 \％。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.

Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing.