FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313525589> ?p ?o ?g. }

• W4313525589 endingPage "17" @default.
• W4313525589 startingPage "1" @default.
• W4313525589 abstract "Object detection in remote-sensing images is a crucial task in the fields of Earth observation and computer vision. Despite impressive progress in modern remote-sensing object detectors, there are still three challenges to overcome: 1) complex background interference; 2) dense and cluttered arrangement of instances; and 3) large-scale variations. These challenges lead to two key deficiencies, namely, coarse features and coarse samples, which limit the performance of existing object detectors. To address these issues, in this article, a novel coarse-to-fine framework (CoF-Net) is proposed for object detection in remote-sensing imagery. CoF-Net mainly consists of two parallel branches, namely, coarse-to-fine feature adaptation (CoF-FA) and coarse-to-fine sample assignment (CoF-SA), which aim to progressively enhance feature representation and select stronger training samples, respectively. Specifically, CoF-FA smoothly refines the original coarse features into multispectral nonlocal fine features with discriminative spatial–spectral details and semantic relations. Meanwhile, CoF-SA dynamically considers samples from coarse to fine by progressively introducing geometric and classification constraints for sample assignment during training. Comprehensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed method." @default.
• W4313525589 created "2023-01-06" @default.
• W4313525589 creator A5019155476 @default.
• W4313525589 creator A5019678322 @default.
• W4313525589 creator A5078114143 @default.
• W4313525589 date "2023-01-01" @default.
• W4313525589 modified "2023-10-16" @default.
• W4313525589 title "CoF-Net: A Progressive Coarse-to-Fine Framework for Object Detection in Remote-Sensing Imagery" @default.
• W4313525589 cites W1536680647 @default.
• W4313525589 cites W1903029394 @default.
• W4313525589 cites W1984434739 @default.
• W4313525589 cites W2005368619 @default.
• W4313525589 cites W2017448754 @default.
• W4313525589 cites W2031614119 @default.
• W4313525589 cites W2085625911 @default.
• W4313525589 cites W2100503224 @default.
• W4313525589 cites W2102605133 @default.
• W4313525589 cites W2161969291 @default.
• W4313525589 cites W2194775991 @default.
• W4313525589 cites W2512351403 @default.
• W4313525589 cites W2565639579 @default.
• W4313525589 cites W2570343428 @default.
• W4313525589 cites W2594177559 @default.
• W4313525589 cites W2601564443 @default.
• W4313525589 cites W2737725206 @default.
• W4313525589 cites W2752782242 @default.
• W4313525589 cites W2779335303 @default.
• W4313525589 cites W2788202095 @default.
• W4313525589 cites W2902156419 @default.
• W4313525589 cites W2912514498 @default.
• W4313525589 cites W2935079508 @default.
• W4313525589 cites W2962749812 @default.
• W4313525589 cites W2963091558 @default.
• W4313525589 cites W2963150697 @default.
• W4313525589 cites W2963179609 @default.
• W4313525589 cites W2963351448 @default.
• W4313525589 cites W2963857746 @default.
• W4313525589 cites W2964979676 @default.
• W4313525589 cites W2966926453 @default.
• W4313525589 cites W2973030851 @default.
• W4313525589 cites W2982770724 @default.
• W4313525589 cites W2987761193 @default.
• W4313525589 cites W2991359031 @default.
• W4313525589 cites W2991363140 @default.
• W4313525589 cites W2992240579 @default.
• W4313525589 cites W2995801068 @default.
• W4313525589 cites W2999452014 @default.
• W4313525589 cites W3009818596 @default.
• W4313525589 cites W3012573144 @default.
• W4313525589 cites W3015331846 @default.
• W4313525589 cites W3034552520 @default.
• W4313525589 cites W3035396860 @default.
• W4313525589 cites W3046174881 @default.
• W4313525589 cites W3092088889 @default.
• W4313525589 cites W3098218837 @default.
• W4313525589 cites W3101126838 @default.
• W4313525589 cites W3102701618 @default.
• W4313525589 cites W3109055651 @default.
• W4313525589 cites W3111355700 @default.
• W4313525589 cites W3113728373 @default.
• W4313525589 cites W3117344638 @default.
• W4313525589 cites W3121842289 @default.
• W4313525589 cites W3122686701 @default.
• W4313525589 cites W3133630855 @default.
• W4313525589 cites W3136761610 @default.
• W4313525589 cites W3141404903 @default.
• W4313525589 cites W3147835263 @default.
• W4313525589 cites W3156315514 @default.
• W4313525589 cites W3162930275 @default.
• W4313525589 cites W3174389852 @default.
• W4313525589 cites W3175496347 @default.
• W4313525589 cites W3176495987 @default.
• W4313525589 cites W3177105943 @default.
• W4313525589 cites W3180084293 @default.
• W4313525589 cites W3183129439 @default.
• W4313525589 cites W3193551430 @default.
• W4313525589 cites W3193627072 @default.
• W4313525589 cites W3201260569 @default.
• W4313525589 cites W3203170932 @default.
• W4313525589 cites W3209073849 @default.
• W4313525589 cites W3213833596 @default.
• W4313525589 cites W4205659311 @default.
• W4313525589 cites W4205932092 @default.
• W4313525589 cites W4210925408 @default.
• W4313525589 cites W4214560328 @default.
• W4313525589 cites W4214648418 @default.
• W4313525589 cites W4214666412 @default.
• W4313525589 cites W4225512839 @default.
• W4313525589 cites W4294811189 @default.
• W4313525589 cites W4294982749 @default.
• W4313525589 cites W639708223 @default.
• W4313525589 doi "https://doi.org/10.1109/tgrs.2022.3233881" @default.
• W4313525589 hasPublicationYear "2023" @default.
• W4313525589 type Work @default.
• W4313525589 citedByCount "10" @default.
• W4313525589 countsByYear W43135255892023 @default.
• W4313525589 crossrefType "journal-article" @default.
• W4313525589 hasAuthorship W4313525589A5019155476 @default.

• next