Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection

Object detection on VHR remote sensing images plays a vital role in applications such as urban planning, land resource management, and rescue missions. The large-scale variation of the remote-sensing targets is one of the main challenges in VHR remote-sensing object detection. Existing methods improve the detection accuracy of high-resolution remote sensing objects by improving the structure of feature pyramids and adopting different attention modules. However, for small targets, there still be seriously missed detections due to the loss of key detail features. There is still room for improvement in the way of multiscale feature fusion and balance. To address this issue, this paper proposes two novel modules: Guided Attention and Tucker Bilinear Attention, which are applied to the stages of early fusion and late fusion respectively. The former can effectively retain clean key detail features, and the latter can better balance features through semantic-level correlation mining. Based on two modules, we build a new multi-scale remote sensing object detection framework. No bells and whistles. The proposed method largely improves the average precisions of small objects and achieves the highest mean average precisions compared with 9 state-of-the-art methods on DOTA, DIOR, and NWPU VHR-10.Code and models are available at https://github.com/Shinichict/GTNet.Comment: arXiv admin note: text overlap with arXiv:1705.06676, arXiv:2209.13351 by other author

Fair Federated Medical Image Segmentation via Client Contribution Estimation

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies.Comment: Accepted at CVPR 202

Swin transformers make strong contextual encoders for VHR image road extraction

Significant progress has been made in automatic road extra-ction or segmentation based on deep learning, but there are still margins to improve in terms of the completeness and connectivity of the results. This is mainly due to the challenges of large intra-class variances, ambiguous inter-class distinctions, and occlusions from shadows, trees, and buildings. Therefore, being able to perceive global context and model geometric information is essential to further improve the accuracy of road segmentation. In this paper, we design a novel dual-branch encoding block CoSwin which exploits the capability of global context modeling of Swin Transformer and that of local feature extraction of ResNet. Furthermore, we also propose a context-guided filter block named CFilter, which can filter out context-independent noisy features for better reconstructing of the details. We use CoSwin and CFilter in a U-shaped network architecture. Experiments on Massachusetts and CHN6-CUG datasets show that the proposed method outperforms other state-of-the-art methods on the metrics of F1, IoU, and OA. Further analysis reveals that the improvement in accuracy comes from better integrity and connectivity of segmented roads

Identification, evolution, and expression partitioning of miRNAs in allopolyploid <em>Brassica napus</em>

International audienceThe recently published genome of Brassica napus offers for the first time the opportunity to gain insights into the genomic organization and the evolution of miRNAs in oilseed rape. In this study, 12 small RNA libraries from two B. napus cultivars (Tapidor and Ningyou7) and their four double-haploid lines were sequenced, employing the newly sequenced B. napus genome, together with genomes of its progenitors Brassica rapa and Brassica oleracea. A total of 645 miRNAs including 280 conserved and 365 novel miRNAs were identified. Comparative analysis revealed a high level of genomic conservation of MIRNAs (75.9%) between the subgenomes of B. napus and its two progenitors' genomes, and MIRNA lost/gain events (133) occurred in B. napus after its speciation. Furthermore, significant partitioning of miRNA expressions between the two subgenomes in B. napus was detected. The data of degradome sequencing, miRNA-mediated cleavage, and expression analyses support specific interactions between miRNAs and their targets in the modulation of diverse physiological processes in roots and leaves, as well as in biosynthesis of, for example, glucosinolates and lipids in oilseed rape. These data provide a first genome-wide view on the origin, evolution, and genomic organization of B. napus MIRNAs

Identification, evolution, and expression partitioning of miRNAs in allopolyploid Brassica napus

The recently published genome of Brassica napus offers for the first time the opportunity to gain insights into the genomic organization and the evolution of miRNAs in oilseed rape. In this study, 12 small RNA libraries from two B. napus cultivars (Tapidor and Ningyou7) and their four double-haploid lines were sequenced, employing the newly sequenced B. napus genome, together with genomes of its progenitors Brassica rapa and Brassica oleracea. A total of 645 miRNAs including 280 conserved and 365 novel miRNAs were identified. Comparative analysis revealed a high level of genomic conservation of MIRNAs (75.9%) between the subgenomes of B. napus and its two progenitors’ genomes, and MIRNA lost/gain events (133) occurred in B. napus after its speciation. Furthermore, significant partitioning of miRNA expressions between the two subgenomes in B. napus was detected. The data of degradome sequencing, miRNA-mediated cleavage, and expression analyses support specific interactions between miRNAs and their targets in the modulation of diverse physiological processes in roots and leaves, as well as in biosynthesis of, for example, glucosinolates and lipids in oilseed rape. These data provide a first genome-wide view on the origin, evolution, and genomic organization of B. napus MIRNAs