Few-Shot Object Detection (FSOD) aims to recognize novel class targets using limited annotated data. Conventional approaches rely on extensive base class training, followed by fine-tuning where few instances from both base and novel classes are sampled for each category. Although they demonstrate remarkable performance in natural image domains, the specificity of remote sensing scenarios poses two critical challenges for FSOD: 1) The morphological differences between remote sensing images and natural images are significant, leading to a loss of structural priors in the Region Proposal Network (RPN). This makes it difficult for structural priors pretrained on natural images to generalize to remote sensing images, especially for novel class with scarce data; 2) Differences in imaging conditions lead to appearance variations among similar objects, leading to sparse visual features are insufficient to represent the common semantic structure of the entire class. To solve problems above, we introduce an innovative framework named ST-FSOD. Primarily, we introduce the SA-RPN module, which leverages efficient pixel association capability to generate high-quality foreground object proposals. Subsequently, through a text guiding learner module (TGL), we use textual labels of each category to generate image-agnostic text-guided prototypes. The enhanced text prototypes are fused with visual features to complement the sparse visual features. Extensive experiments conducted on the DIOR, NWPU VHR-10 and RSOD benchmarks demonstrate that the proposed method consistently surpasses strong baselines and achieves superior performance compared to previous state-of-the-art (SOTA) approaches. Our project will be open-sourced soon on https://github.com/wdcjhyy/ST-FSOD.

Entity matching (EM) determines whether two records from different data sources refer to the same real-world entity. It is a fundamental task in knowledge graph construction and data integration. Currently, deep learning (DL) based EM methods have achieved state-of-the-art (SOTA) results. However, apply-ing DL-based EM methods often costs a lot of human efforts to label the data. To address this challenge, we propose a new do-main adaptation (DA) framework for EM called Matching Fea-ture Separation Network (MFSN). We implement DA by sepa-rating private and common matching features. Briefly, MFSN first uses three encoders to explicitly model the private and common matching features in both the source and target do-mains. Then, it transfers the knowledge learned from the source common matching features to the target domain. We also pro-pose an enhanced variant called Feature Representation and Separation Enhanced MFSN (MFSN-FRSE). Compared with MFSN, it has superior feature representation and separation capabilities. We evaluate the effectiveness of MFSN and MFSN-FRSE on twelve DA in EM tasks. The results show that our framework is approximately 7% higher in F1 score on average than the previous SOTA methods. Then, we verify the effec-tiveness of each module in MFSN and MFSN-FRSE by ablation study. Finally, we explore the optimal strategy of each module in MFSN and MFSN-FRSE through detailed tests.

Abstract An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.

With the increasing prevalence of web services on the World Wide Web, a large number of functionally equivalent web services are provided by different providers. Quality-of-Service (QoS), representing the nonfunctional characteristics, plays an important role in dealing with how to recommend the optimal services to users among these candidates. Many existing methods for predicting QoS values of web services show that QoS values are intensively relevant to location due to the great influence of network distance and the internet connection between users and services. In this paper, we propose a novel location-aware feature interaction learning (LAFIL) method for predicting the QoS values of the user-service matrix and then making the recommendation by learning the underlying relation, which is hidden in the features concerning with location information. LAFIL can effectively solve the problems of data sparsity and cold-start by leveraging the location features of both users and services. To evaluate the performance of our proposed method, comprehensive experiments are conducted using a real-world dataset and the results show that our method achieves better QoS prediction accuracy compared to state-of-the-art approaches.

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).