Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.

Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs. However, CCR suffers from two main transitive problems: threshold effect and scene drift. In other words, the causal pairs to be spliced may have a conflicting threshold boundary or scenario. To address these issues, we propose a novel Reliable Causal chain reasoning framework~(ReCo), which introduces exogenous variables to represent the threshold and scene factors of each causal pair within the causal chain, and estimates the threshold and scene contradictions across exogenous variables via structural causal recurrent neural networks~(SRNN). Experiments show that ReCo outperforms a series of strong baselines on both Chinese and English CCR datasets. Moreover, by injecting reliable causal chain knowledge distilled by ReCo, BERT can achieve better performances on four downstream causal-related tasks than BERT models enhanced by other kinds of knowledge.

The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts. However, GWR suffers from the problem that classical linear regressions, which compose the GWR model, are more prone to be underfitting, especially for significant volume and complex nonlinear data, causing inferior comparative performance. Nevertheless, some advanced models, such as the decision tree and the support vector machine, can learn features from complex data more effectively while they cannot provide explainable quantification for the spatial variation of localized relationships. To address the above issues, we propose a geographically gradient boosting weighted regression model, GWRBoost, that applies the localized additive model and gradient boosting optimization method to alleviate underfitting problems and retains explainable quantification capability for spatially-varying relationships between geographically located variables. Furthermore, we formulate the computation method of the Akaike information score for the proposed model to conduct the comparative analysis with the classic GWR algorithm. Simulation experiments and the empirical case study are applied to prove the efficient performance and practical value of GWRBoost. The results show that our proposed model can reduce the RMSE by 18.3\% in parameter estimation accuracy and AICc by 67.3\% in the goodness of fit.

Earth observation, aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With a growing number of satellites in orbit, an increasing number of datasets with diverse sensors and research domains are being published to facilitate the research of the remote sensing community. In this paper, we present a comprehensive review of more than 400 publicly published datasets, including applications like land use/cover, change/disaster monitoring, scene understanding, agriculture, climate change, and weather forecasting. We systematically analyze these Earth observation datasets with respect to five aspects volume, bibliometric analysis, resolution distributions, research domains, and the correlation between datasets. Based on the dataset attributes, we propose to measure, rank, and select datasets to build a new benchmark for model evaluation. Furthermore, a new platform for Earth observation, termed EarthNets, is released as a means of achieving a fair and consistent evaluation of deep learning methods on remote sensing data. EarthNets supports standard dataset libraries and cutting-edge deep learning models to bridge the gap between the remote sensing and machine learning communities. Based on this platform, extensive deep learning methods are evaluated on the new benchmark. The insightful results are beneficial to future research. The platform and dataset collections are publicly available at https://earthnets.github.io/.

资金机构在很大程度上依赖于领域专家与研究建议之间的主题匹配来分配提案审查员。随着建议越来越跨学科，概述提案的跨学科性质是一项挑战，此后，找到具有适当专业知识的专家审阅者。解决这一挑战的重要步骤是准确对建议的跨学科标签进行分类。现有的方法论和申请相关文献，例如文本分类和提案分类，不足以共同解决跨学科建议数据引入的三个关键独特问题：1）提案的纪律标签的层次结构，谷物，例如，从信息科学到AI，再到AI的基础。 2）在提案中起着不同作用的各种主要文本部分的异质语义； 3）提案的数量在非学科和跨学科研究之间存在不平衡。我们可以同时解决该提案的跨学科性质时的三个问题吗？为了回答这个问题，我们提出了一个层次混音多标签分类框架，我们称之为H-Mixup。 H-Mixup利用基于变压器的语义信息提取器和基于GCN的跨学科知识提取器来解决第一期和第二个问题。 H-Mixup开发了Wold级混音，Word级cutmix，歧管混音和文档级混音的融合训练方法，以解决第三期。

The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

场景图生成（SGG）任务旨在在给定图像中检测所有对象及其成对的视觉关系。尽管SGG在过去几年中取得了显着的进展，但几乎所有现有的SGG模型都遵循相同的训练范式：他们将SGG中的对象和谓词分类视为单标签分类问题，而地面真实性是一个hot目标。标签。但是，这种普遍的训练范式忽略了当前SGG数据集的两个特征：1）对于正样本，某些特定的主题对象实例可能具有多个合理的谓词。 2）对于负样本，有许多缺失的注释。不管这两个特征如何，SGG模型都很容易被混淆并做出错误的预测。为此，我们为无偏SGG提出了一种新颖的模型不合命相的标签语义知识蒸馏（LS-KD）。具体而言，LS-KD通过将预测的标签语义分布（LSD）与其原始的单热目标标签融合来动态生成每个主题对象实例的软标签。 LSD反映了此实例和多个谓词类别之间的相关性。同时，我们提出了两种不同的策略来预测LSD：迭代自我KD和同步自我KD。大量的消融和对三项SGG任务的结果证明了我们所提出的LS-KD的优势和普遍性，这些LS-KD可以始终如一地实现不同谓词类别之间的不错的权衡绩效。

最近，越来越多的努力集中在弱监督的场景图（WSSGG）上。 WSSGG的主流解决方案通常遵循相同的管道：它们首先将文本实体与弱图像级别的监督（例如，未定位的关系三胞胎或字幕）相结合，然后用图像区域对齐，然后以完全固定的实例训练SGG模型 - 级别的“伪”标签。但是，我们认为大多数现有的WSSGG仅专注于对象一致性，这意味着接地区域应具有与文本实体相同的对象类别标签。尽管他们忽略了理想对齐的另一个基本要求：相互作用，这意味着接地区域对应具有与文本实体对相同的相互作用（即视觉关系）。因此，在本文中，我们建议通过使用对象感知和互动感知知识来增强简单的接地模块，以获取更可靠的伪标签。为了更好地利用这两种类型的知识，我们将它们视为两位老师，并融合其生成的目标，以指导我们接地模块的训练过程。具体而言，我们设计了两种不同的策略，可以通过评估每个培训样本的可靠性来适应不同的教师。广泛的实验表明，我们的方法始终在各种弱监督下提高WSSGG性能。

几乎所有现有的场景图（SGG）模型都忽略了主流SGG数据集的地面真相注释质量，即他们假设：1）所有手动注释的正样本都是同样正确的； 2）所有未注销的负样本绝对是背景。在本文中，我们认为这两个假设都不适用于SGG：有许多嘈杂的地面谓词标签破坏了这两个假设并损害了无偏SGG模型的训练。为此，我们提出了一种新颖的嘈杂标签校正和SGG：最佳的样本训练策略。具体而言，它包括两个部分：尼斯和NIST，它们分别通过产生高质量的样本和有效的培训策略来排除这些嘈杂的标签问题。 NICE首先检测到嘈杂的样品，然后将它们重新分配给它们更多高质量的软谓词标签。 NIST是一种基于多教老师知识蒸馏的培训策略，它使模型能够学习公正的融合知识。 NIST的动态权衡加权策略旨在惩罚不同教师的偏见。由于NICE和NIST的模型不足的性质，我们最好的最好的人可以无缝地纳入任何SGG架构中，以提高其在不同谓词类别上的性能。此外，为了更好地评估SGG模型的概括，我们通过重新组织普遍的VG数据集并故意使培训和测试集的谓词分布尽可能不同，进一步提出了一种新的基准VG-OOD。对象类别对。这种新的基准有助于解散基于对象类别类别的频率偏差的影响。大量消融和对不同的骨干和任务的结果证明了最佳组成部分的有效性和概括能力。