Weakly-Supervised Salient Object Detection via Scribble Annotations

Zhang, Jing; Yu, Xin; Li, Aixuan; Song, Peipei; Liu, Bowen; Dai, Yuchao

doi:10.1109/cvpr42600.2020.01256

Cited by 240 publications

(211 citation statements)

References 40 publications

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“…To investigate the infection segmentation performance of the proposed USTM-Net, we compared it with other five state-of-the-art methods, including Scribble2Label (S2L) model [42] , weakly-supervised salient object detection (WSOD) method [52] , partial U-Net (p-UNet) [53] , weakly-supervised consistency-based learning (WSCL) method [16] , and U-Net [47] . The first three methods are weakly supervised learning methods using scribbled annotations, and the fourth method is a weakly supervised method using point-level annotations.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The best results are highlighted in bold. Label Methods DI JA SE SP MAE Scribble p-UNet [53] 0.683 0.531 0.837 0.836 0.127 WSOD [52] 0.706 0.558 0.871 0.885 0.093 S2L [42] 0.745 0.607 0.843 0.956 0.087 USTM -Net (proposed) 0.762 0.628 0.882 0.984 0.076 Point WSCL [16] 0.719 0.573 0.826 0.929 0.114 Full U-Net [47] 0.788 0.663 0.885 0.978 0.071 …”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The best results are highlighted in bold. Label Methods DI JA SE SP MAE Scribble p-UNet [53] 0.660 0.516 0.833 0.825 0.138 WSOD [52] 0.684 0.533 0.842 0.871 0.114 S2L [42] 0.708 0.550 0.805 0.926 0.091 USTM-Net (proposed) 0.725 0.582 0.854 0.967 0.086 Full U-Net [47] 0.736 0.595 0.867 0.961 0.082 …”

Section: Experiments and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Liu

Yuan

Gao

et al. 2022

Pattern Recognition

107

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Segmentation of infections from CT scans is important for accurate diagnosis and follow-up in tackling the COVID-19. Although the convolutional neural network has great potential to automate the segmentation task, most existing deep learning-based infection segmentation methods require fully annotated ground-truth labels for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised segmentation method for COVID-19 infections in CT slices, which only requires scribble supervision and is enhanced with the uncertainty-aware self-ensembling and transformation-consistent techniques. Specifically, to deal with the difficulty caused by the shortage of supervision, an uncertainty-aware mean teacher is incorporated into the scribble-based segmentation method, encouraging the segmentation predictions to be consistent under different perturbations for an input image. This mean teacher model can guide the student model to be trained using information in images without requiring manual annotations. On the other hand, considering the output of the mean teacher contains both correct and unreliable predictions, equally treating each prediction in the teacher model may degrade the performance of the student network. To alleviate this problem, the pixel level uncertainty measure on the predictions of the teacher model is calculated, and then the student model is only guided by reliable predictions from the teacher model. To further regularize the network, a transformation-consistent strategy is also incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed method has been evaluated on two public datasets and one local dataset. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Liu

Yuan

Gao

et al. 2022

Pattern Recognition

107

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show abstract

“…F-measure (F β ) [30] and Structure Measure(S m ) [31] are also used to evaluate salient maps. Following [32], we use B µ to evaluate the structure alignment between saliency maps and their ground-truth, which shows the boundary quality of predictions. Smaller MAE and B µ , larger F β and S m correspond to better performance.…”

Section: Methodsmentioning

confidence: 99%

Fast: Feature Aggregation for Detecting Salient Object in Real-Time

Tang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper introduces a method named FAST for real-time salient object detection with an extremely efficient CNN architecture. Our proposed network starts from a single lightweight backbone and aggregates discriminative features through network-level and phase-level respectively. Based on the multi-scale feature propagation, FAST substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and performance. To better preserve object boundaries, we also explore the complementary between salient object information and edge information within our lightweight architecture. Extensive evaluations and analysis demonstrate that the proposed algorithm achieves the leading accuracy performance with real-time speed (186fps) which is significantly faster than the existing state-of-the-art methods.

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“…The deep network also plays an important role in this field. The variational autoencoder [21,22], the adaptive window and weight matching algorithm [23], the deep purifier, and the feature learning unit greatly improve the accuracy of detection. However, deep learning requires more sophisticated hardware and relies on a large number of training samples [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images

Gong

et al. 2020

Sensors

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To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.

show abstract

Weakly-Supervised Salient Object Detection via Scribble Annotations

Cited by 240 publications

References 40 publications

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Fast: Feature Aggregation for Detecting Salient Object in Real-Time

Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images

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