Scribble-attention hierarchical network for weakly supervised salient object detection in optical remote sensing images

Ma, Lei; Luo, Xingfang; Hu, Hong; Zhang, Yaozong; Wang, Lei; Wu, Jinmeng

doi:10.1007/s10489-022-04014-0

Cited by 14 publications

(1 citation statement)

References 68 publications

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“…Both the global and local imagelevel contexts were used to boost the performance of WSOD. Ma et al [40] re-labeled an optical RSI dataset in a scribble manner and proposed a scribble embedding network (SEN) to extract discriminant regions in images. SEN unified correlation filtering technology into the proposed WSOD network, which successfully emphasizes the foreground features and suppresses the cluttered background.…”

Section: Feature Refinement For Wsod In Rsis 221 Image-level Feature ...mentioning

confidence: 99%

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

Zheng,

Wu,

et al. 2024

Remote Sensing

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Weakly supervised object detection (WSOD) aims to predict a set of bounding boxes and corresponding category labels for instances with only image-level supervisions. Compared with fully supervised object detection, WSOD in remote sensing images (RSIs) is much more challenging due to the vast foreground-related context regions. In this paper, we propose a progressive image-level and instance-level feature refinement network to address the problems of missing detection and part domination for WSOD in RSIs. Firstly, we propose a multi-label attention mining loss (MAML)-guided image-level feature refinement branch to effectively allocate the computational resources towards the most informative part of images. With the supervision of MAML, all latent instances in images are emphasized. However, image-level feature refinement further expands responsive gaps between the informative part and other sub-optimal informative ones, which results in exacerbating the problem of part domination. In order to alleviate the above-mentioned limitation, we further construct an instance-level feature refinement branch to re-balance the contributions of different adjacent candidate bounding boxes according to the detection task. An instance selection loss (ISL) is proposed to progressively boost the representation of salient regions by exploring supervision from the network itself. Finally, we integrate the image-level and instance-level feature refinement branches into a complete network and the proposed MAML and ISL functions are merged with class classification and box regression to optimize the whole WSOD network in an end-to-end training fashion. We conduct experiments on two popular WSOD datasets, NWPU VHR-10.v2 and DIOR. All the experimental results demonstrate that our method achieves a competitive performance compared with other state-of-the-art approaches.

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Section: Feature Refinement For Wsod In Rsis 221 Image-level Feature ...mentioning

confidence: 99%

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

Zheng,

Wu,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network

Ma,

Xu,

Hong

et al. 2023

Appl Intell

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DDABNet: a dense Do-conv residual network with multisupervision and mixed attention for image deblurring

Shi,

Huang,

Chen

et al. 2023

Appl Intell

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Scribble-attention hierarchical network for weakly supervised salient object detection in optical remote sensing images

Cited by 14 publications

References 68 publications

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network

DDABNet: a dense Do-conv residual network with multisupervision and mixed attention for image deblurring

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