Federated data selection strategically chooses a group of high-quality samples to train a global model, and it is promising to optimize the convergence and resource overhead of federated learning (FL). However, existing studies either fail to account for the dynamic importance of training samples or rely on external unbiased validation datasets. These shortcomings can compromise FL model performance, potentially complicating their application in real-world scenarios. In this paper, we propose a novel proxy-validated importance-aware federated sample selection framework, termed FedSelect. It employs a novel meta learning approach with a proxy validation dataset to select the most positively important clients and their most important samples, thereby accelerating the training process and optimizing FL model performance. To eliminate the dependency on external unbiased data, we present a momentum-based meta-margin function to discover influential samples as the proxy validation dataset, providing an adaptive reward for sample selection. Furthermore, we also develop an online meta model update strategy to guarantee the efficiency of FedSelect. Comprehensive experiments on four benchmark datasets demonstrate that FedSelect is superior in both effectiveness and efficiency, while maintaining strong scalability across diverse scenarios. The source code can be accessed at: https://github.com/nameyzhang/FedSelect.

The long-tail problem presents a significant challenge to the advancement of semantic segmentation in ultra-high-resolution (UHR) satellite imagery. While previous efforts in UHR semantic segmentation have largely focused on multibranch network architectures that emphasize multi-scale feature extraction and fusion, they have often overlooked the importance of addressing the long-tail issue. In contrast to prior UHR methods that focused on independent feature extraction, we emphasize data augmentation and multimodal feature fusion to alleviate the long-tail problem. In this paper, we introduce SRMF, a novel framework for semantic segmentation in UHR satellite imagery. Our approach addresses the long-tail class distribution by incorporating a multi-scale cropping technique alongside a data augmentation strategy based on semantic reordering and resampling. To further enhance model performance, we propose a multimodal fusion-based general representation knowledge injection method, which, for the first time, fuses text and visual features without the need for individual region text descriptions, extracting more robust features. Extensive experiments on the URUR, GID, and FBP datasets demonstrate that our method improves mIoU by 3.33%,0.66%, and 0.98%, respectively, achieving state-of-the-art performance. Code is available at: https://github.com/BinSpa/SRMF.git.

Quantum readout noise turns out to be the most significant source of error, which greatly affects the measurement fidelity. Matrix-based calibration has been demonstrated to be effective in various quantum platforms. However, existing methodologies are fundamentally limited in either scalability or accuracy. Inspired by the classical finite element method (FEM), a formal method to model the complex interaction between elements, we present our calibration framework named QuFEM. First, we apply a divide-and-conquer strategy that formulates the calibration as a series of tensor products with noise matrices. This matrices are iteratively characterized together with the calibrated probability distribution, aiming to capture the inherent locality of qubit interactions. Then, to accelerate the end-to-end calibration, we propose a sparse tensor-product engine to exploit the sparsity in the intermediate values. Our experiments show that QuFEM achieves 2.5×103× speedup in the 136-qubit calibration compared to the state-of-the-art matrix-based calibration technique [50], and provides 1.2× and 1.4× fidelity improvement on the 18-qubit and 36-qubit real-world quantum devices.

Unsupervised domain adaptation (UDA) has been a crucial way for cross-domain semantic segmentation of remote sensing images and reached apparent advents. However, most existing efforts focus on single source single target domain adaptation, which don't explicitly consider the serious domain shift between multiple source and target domains in real applications, especially inter-domain shift between various target domains and intra-domain shift within each target domain. In this paper, to address simultaneous inter-domain shift and intra-domain shift for multiple target domains, we propose a novel unsupervised, multistage, multisource and multitarget domain adaptation network (MultiDAN), which involves multisource and multitarget domain adaptation (MSMTDA), entropy-based clustering (EC) and multistage domain adaptation (MDA). Specifically, MSMTDA learns feature-level multiple adversarial strategies to alleviate complex domain shift between multiple target and source domains. Then, EC clusters the various target domains into multiple subdomains based on entropy of target predictions of MSMTDA. Besides, we propose a new pseudo label update strategy (PLUS) to dynamically produce more accurate pseudo labels for MDA. Finally, MDA aligns the clean subdomains, including pseudo labels generated by PLUS, with other noisy subdomains in the output space via the proposed multistage adaptation algorithm (MAA). The extensive experiments on the benchmark remote sensing datasets highlight the superiority of our MultiDAN against recent state-of-the-art UDA methods.

Propositional satisfiability problem (SAT) is represented in a conjunctive normal form with multiple clauses, which is an important non-deterministic polynomial-time (NP) complete problem that plays a major role in various applications including artificial intelligence, graph colouring, and circuit analysis. Quantum annealing (QA) is a promising methodology for solving complex SAT problems by exploiting the parallelism of quantum entanglement, where the SAT variables are embedded to the qubits. However, the long embedding time fundamentally limits existing QA-based methods, leading to inefficient hardware implementation and poor scalability.In this paper, we propose HyQSAT, a hybrid approach that integrates QA with the classical Conflict-Driven Clause Learning (CDCL) algorithm to enable end-to-end acceleration for solving SAT problems. Instead of embedding all clauses to QA hardware, we quantitatively estimate the conflict frequency of clauses and apply breadth-first traversal to choose their embedding order. We also consider the hardware topology to maximize the utilization of physical qubits in embedding to QA hardware. Besides, we adjust the embedding coefficients to improve the computation accuracy under qubit noise. Finally, we present how to interpret the satisfaction probability based on QA energy distribution and use this information to guide the CDCL search. Our experiments demonstrate that HyQSAT can effectively support larger-scale SAT problems that are beyond the capability of existing QA approaches, achieve up to 12.62X end-to-end speedup using D-Wave 2000Q compared to the classic CDCL algorithm on Intel E5 CPU, and considerably reduce the QA embedding time from 17.2s to 15.7µs compared to the D-Wave Minorminer algorithm [11].

In the current Noisy Intermediate-Scale Quantum era, quantum circuit analysis is an essential technique for designing high-performance quantum programs. Current analysis methods exhibit either accuracy limitations or high computational complexity for obtaining precise results. To reduce this tradeoff, we propose QuCT, a unified framework for extracting, analyzing, and optimizing quantum circuits. The main innovation of QuCT is to vectorize each gate with each element, quantitatively describing the degree of the interaction with neighboring gates. Extending from the vectorization model, we propose two representative downstream models for fidelity prediction and unitary decomposition. The fidelity prediction model performs a linear transformation on all gate vectors and aggregates the results to estimate the overall circuit fidelity. By identifying critical weights in the transformation matrix, we propose two optimizations to improve the circuit fidelity. In the unitary decomposition model, we significantly reduce the search space by bridging the gap between unitary and circuit via gate vectors. Experiments show that QuCT improves the accuracy of fidelity prediction by 4.2 × on 5-qubit and 18-qubit quantum devices and achieves 2.5 × fidelity improvement compared to existing quantum compilers [19, 55]. In unitary decomposition, QuCT achieves 46.3 × speedup for 5-qubit unitary and more than hundreds of speedup for 8-qubit unitary, compared to the state-of-the-art method [87].

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to one target domain. Although multi-source domains contain more complementary information than single source domain, MSDA involves some disturbed source samples, which will degrade the adaptation performance. To solve this problem, we propose a novel MSDA method for semantic segmentation. Specifically, to fully explore the optimal source samples for target domain, we propose a novel correlation measurement mechanism, weighing domain-level source-target correlation (DSC) and pixel-level source-target correlation (PSC). For each pair of source and target domains, DSC and PSC estimate the source-target correlations via the distances between target class prototypes and source class prototypes, and between target class prototypes and every pixel of source features, respectively. Built upon PSC, we propose a novel mix-up strategy, which pastes high-correlation source pixels to target images, to construct augmented mixing images for adaptation. Then we train the segmentor on the mixed images with pseudo labels and labeled source images, with DSC and PSC to suppress the negative effects of the low-correlation source domains and pixels. Furthermore, an attentive prototype alignment loss, based on DSC, is proposed to align target and multi-source domains, which attaches more importance to high-correlation source domains. The experimental results on the representative benchmark datasets (i.e., GTA5 and SYNTHIA → Cityscapes) highlight that our method substantially outperforms the state-of-the-art single-source domain adaptation and MSDA methods.

Unsupervised domain adaptation (UDA) has become an important technique for cross-domain semantic segmentation (SS) in the remote sensing community and obtained remarkable results. However, when transferring from high-resolution (HR) remote sensing images to low-resolution (LR) images, the existing UDA frameworks always fail to segment the LR target images, especially for small objects (e.g., cars), due to the severe spatial resolution shift problem. In this article, to improve the segmentation ability of UDA models for LR target images and small objects, we propose a novel multitask domain adaptation network (DASRSNet) for SS of remote sensing images with the aid of super-resolution (SR). The proposed DASRSNet contains domain adaptation for SS (DASS) branch, domain adaptation for SR (DASR) branch, and feature affinity (FA) module. Specifically, the DASS and DASR branches share the same encoder to extract the domain-invariant features for the target and source domains, and these two branches utilize different decoders and discriminators to conduct cross-domain SS task and SR task, which align the domain shift in output space and image space, respectively. Finally, the FA module, which involves the proposed FA loss, is applied to enhance the affinity of SS features and SR features for both source and target domains. The experimental results on the cross-city aerial datasets demonstrate the effectiveness and superiority of our DASRSNet against the recent UDA models.

Computational photo quality evaluation is a useful technique in many tasks of computer vision and graphics, for example, photo retaregeting, 3-D rendering, and fashion recommendation. The conventional photo quality models are designed by characterizing the pictures from all communities (e.g., "architecture" and "colorful") indiscriminately, wherein community-specific features are not exploited explicitly. In this article, we develop a new community-aware photo quality evaluation framework. It uncovers the latent community-specific topics by a regularized latent topic model (LTM) and captures human visual quality perception by exploring multiple attributes. More specifically, given massive-scale online photographs from multiple communities, a novel ranking algorithm is proposed to measure the visual/semantic attractiveness of regions inside each photograph. Meanwhile, three attributes, namely: 1) photo quality scores; weak semantic tags; and inter-region correlations, are seamlessly and collaboratively incorporated during ranking. Subsequently, we construct the gaze shifting path (GSP) for each photograph by sequentially linking the top-ranking regions from each photograph, and an aggregation-based CNN calculates the deep representation for each GSP. Based on this, an LTM is proposed to model the GSP distribution from multiple communities in the latent space. To mitigate the overfitting problem caused by communities with very few photographs, a regularizer is incorporated into our LTM. Finally, given a test photograph, we obtain its deep GSP representation and its quality score is determined by the posterior probability of the regularized LTM. Comparative studies on four image sets have shown the competitiveness of our method. Besides, the eye-tracking experiments have demonstrated that our ranking-based GSPs are highly consistent with real human gaze movements.

When segmenting the continuous proliferation of unlabeled remotely sensed images, unsupervised domain adaptation (UDA) has become one of the most critical techniques and achieved significant performance. But in fact, there still exists a large performance gap between the existing UDA frameworks and supervised learning methods, for the majority of UDA frameworks don’t consider the intra-domain gap in the target domain. In this paper, to further minimize the complex intra-domain shift within the target domain in remote sensing, we propose a novel iterative intra-domain adaptation framework (IterDANet), which conducts inter-domain adaptation (InterDA), entropy-based ranking (ER) and iterative intra-domain adaptation (IntraDA). Specifically, first, to enhance the performance of InterDA built upon GAN-based image-to-image translation, we propose a new generator selection strategy to assess and choose a well-trained generator for the inter-domain classifier. Then, to produce more accurate pseudo labels for IntraDA, we propose a new pseudo label generation strategy to remove both high-entropy and low-confident pixels in predicted maps of inter-domain classifier. Finally, to better reduce the intra-domain gap, we propose to cluster all the target images into multiple subdomains using ER and iteratively align the cleanest subdomain with other noisy subdomains. The extensive experiments on the benchmark dataset, which includes cross-city aerial images, highlight the superiority and effectiveness of our IterDANet against the state-of-the-art UDA frameworks.

Instance segmentation is an important method for high-resolution remote sensing images (HRRSIs) analysis. Traditional instance segmentation algorithms are not suitable to analyze complex HRRSIs that exhibit: 1) various shapes and sizes of targets; 2) a large number of small targets; and 3) data with long tail distribution. Here we introduce DB-BlendMask, an efficient and accurate instance segmentation method that can accommodate complex HRRSIs. It is composed of size balance coefficient (SBC), class balance module (CBM), and decomposed attention blender module (DA-Blender module). SBC consists of a fair weight allocation strategy for positive samples in object detection. CBM combines classification obtained in object detection stage to guide the semantic feature extraction. Complementary to a traditional convolutional neural network (CNN) architecture, DA-Blender module has the ability to considerably compress space complexity of attention and merge attention with semantic feature to generate the instance mask. We compare the performance of DB-BlendMask with a benchmark Mask R-CNN on two typical datasets, iSAID, and ISPRS Postdam. We obtain an average detection precision of 39.2% on iSAID and 63.6% on ISPRS Postdam, which corresponds to an improvement of 2.5% and 2.7%, respectively, compared to the benchmark in a real-time scenario.

Effectively recognizing different sceneries with complex backgrounds and varied lighting conditions plays an important role in modern AI systems. Competitive performance has recently been achieved by the deep scene categorization models. However, these models implicitly hypothesize that the image-level labels are 100% correct, which is too restrictive. Practically, the image-level labels for massive-scale scenery sets are usually calculated by external predictors such as ImageNet-CN. These labels can easily become contaminated because no predictors are completely accurate. This article proposes a new deep architecture that calculates scene categories by hierarchically deriving stable templates, which are discovered using a generative model. Specifically, we first construct a semantic space by incorporating image-level labels using subspace embedding. Afterward, it is noticeable that in the semantic space, the superpixel distributions from identically labeled images remain unchanged, regardless of the image-level label noises. On the basis of this observation, a probabilistic generative model learns the stable templates for each scene category. To deeply represent each scenery category, a novel aggregation network is developed to statistically concatenate the CNN features learned from scene annotations predicted by HSA. Finally, the learned deep representations are integrated into an image kernel, which is subsequently incorporated into a multiclass SVM for distinguishing scene categories. Thorough experiments have shown the performance of our method. As a byproduct, an empirical study of 33 SIFT-flow categories shows that the learned stable templates remain almost unchanged under a nearly 36% image label contamination rate.

Accurately categorizing Flickr images into multiple pre-defined communities (e.g., “architecture” and “peaceful”) is an indispensable technique in multimedia analysis, graphic design, fashion recommendation, etc. In practice, these communities are constructed and updated manually, which is subjective and intolerably time consuming. To alleviate these shortcomings, a noise-refined deep matrix factorization (MF) framework is proposed to intelligently discover communities from million-scale Flickr users, wherein the semantic tag correlations and community correlations are simultaneously encoded. More specifically, it is believable that Flickr communities are high-level clues on the basis of human visual semantic perception. Thereby, a MF algorithm is employed to approximate the community label matrix by the product of pairwise factor matrices, which represent the latent representations of user-provided tags and the corresponding basis matrix respectively. Subsequently, an end-to-end deep model is formulated to hierarchically derive the latent deep representation from raw image pixels to semantic tags. To robustly handle contaminated image semantic tags and community labels, an l1norm constraint is encoded to enhance the MF. Meanwhile, to optimally exploit the rich context information of Flickr images, the intrinsic structure between image semantic tags and between communities are collaboratively captured. Finally, the upgraded MF and the deep model are seamlessly combined into a unified framework, which is solved by an iterative algorithm. Experiments on 2 M Flickr images have demonstrated the superiority of our approach. Besides, the discovered Flickr communities can improve photo retargeting and visual aesthetics assessment significantly.