Cascaded Partial Decoder for Fast and Accurate Salient Object Detection

Wu, Zhe; Su, Li; Huang, Qingming

doi:10.1109/cvpr.2019.00403

Cited by 875 publications

(702 citation statements)

References 46 publications

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“…Parallel Partial Decoder: Several existing medical image segmentation networks segment interested organs/lesions using all high-and low-level features in the encoder branch [57], [58], [62]- [65]. However, Wu et al [66] pointed out that, compared with high-level features, low-level features demand more computational resources due to larger spatial resolutions, but contribute less to the performance. Inspired by this observation, we propose to only aggregate high-level features with a parallel partial decoder component, illustrated in Fig.…”

Section: Down-sample×4mentioning

confidence: 99%

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Fan

Zhou

et al. 2020

Preprint

214

374

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Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-theart performance.

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Section: Down-sample×4mentioning

confidence: 99%

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Fan

Zhou

et al. 2020

Preprint

214

374

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“…Recently, to extract more sophisticated features, tremendous deep learning based saliency detectors have been proposed [19]- [25], [39]- [41], and achieved substantially better performance than those previous methods. For example, Lee et al [23] proposed to first encode low-level distance map and high-level sematic features of deep CNNs to form a new feature vector, and then evaluate saliency by a multi-level fully connected neural network classifier.…”

Section: Related Work a Rgb Salient Object Detectionmentioning

confidence: 99%

RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

Zhang

Huang

Yang

et al. 2020

IEEE Trans. on Image Process.

167

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RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multimodal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the crossmodal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast.

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“…We compare our method with 16 previous state-of-the-art methods, namely MDF [28], RFCN [18], UCF [20], Amulet [13], NLDF [12], DSS [31], BMPM [21], PAGR [50], PiCANet [51], SRM [16], DGRL [32], MLMS [52], AFNet [53], CapSal [54], BASNet [15], and CPD [55]. For a fair comparison, we use the saliency maps provided by the authors.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

CAGNet: Content-Aware Guidance for Salient Object Detection

Mohammadi

Noori

Bahri

et al. 2020

Pattern Recognition

102

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Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results. However, it is still challenging to learn effective features for detecting salient objects in complicated scenarios, in which i) non-salient regions may have "salient-like" appearance; ii) the salient objects may have differentlooking regions. To handle these complex scenarios, we propose a Feature Guide Network which exploits the nature of low-level and high-level features to i) make foreground and background regions more distinct and suppress the non-salient regions which have "salient-like" appearance; ii) assign foreground label to different-looking salient regions.Furthermore, we utilize a Multi-scale Feature Extraction Module (MFEM) for each level of abstraction to obtain multi-scale contextual information. Finally, we design a loss function which outperforms the widely-used Cross-entropy loss. By adopting four different pre-trained models as the backbone, we prove that our method is very general with respect to the choice of the backbone model. Experiments on five challenging datasets demonstrate that our method achieves the state-of-the-art performance in terms of different evaluation metrics. Additionally, our approach contains fewer parameters than the existing ones, does not need any post-processing, and runs fast at a real-time speed of 28 FPS when processing a 480 × 480 image.

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Cascaded Partial Decoder for Fast and Accurate Salient Object Detection

Cited by 875 publications

References 46 publications

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

CAGNet: Content-Aware Guidance for Salient Object Detection

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