Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing

Zhang, Kaihua; Li, Tengpeng; Liu, Bo; Liu, Qingshan

doi:10.1109/cvpr.2019.00321

Cited by 81 publications

(76 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early-fusion techniques [70,89] initially extract a global representation of all the images in the input group, capturing relationships between different images. Conversely, late-fusion techniques [67,92,94,95] are designed to estimate single-image saliency from each input individually, and reciprocally update them in a second phase, based on the extracted information.…”

Section: Methods For Co-saliencymentioning

confidence: 99%

“…Another possible criterion to discriminate among different approaches, is the distinction between deep-learning solutions, and those based on hand-crafted design and traditional techniques. Methods in the deep learning group [67,70,94,95] typically benefit from end-to-end learning, therefore optimizing the final objective of co-saliency estimation regardless of the adopted early-fusion or late-fusion approach. Many are based on the Fully-Convolutional Network (FCN) by Long et al [97] or DeepLab by Chen et al [18], both leveraging the VGG backbone [75].…”

Section: Methods For Co-saliencymentioning

confidence: 99%

“…These include AUC-Judd [54], AUC-Borji [20], AUC-Zhao [59] and AUC-Li [60]. The AUC measure has been used to evaluate saliency estimation under different representations: from fixations, scanpaths, and saliency maps [34,36,[61][62][63][64][65][66] to salient object regions [67][68][69][70].…”

Section: Representation-independent Measuresmentioning

confidence: 99%

See 2 more Smart Citations

Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

Buzzelli¹

2020

Applied Sciences

View full text Add to dashboard Cite

We present a review of methods for automatic estimation of visual saliency: the perceptual property that makes specific elements in a scene stand out and grab the attention of the viewer. We focus on domains that are especially recent and relevant, as they make saliency estimation particularly useful and/or effective: omnidirectional images, image groups for co-saliency, and video sequences. For each domain, we perform a selection of recent methods, we highlight their commonalities and differences, and describe their unique approaches. We also report and analyze the datasets involved in the development of such methods, in order to reveal additional peculiarities of each domain, such as the representation used for the ground truth saliency information (scanpaths, saliency maps, or salient object regions). We define domain-specific evaluation measures, and provide quantitative comparisons on the basis of common datasets and evaluation criteria, highlighting the different impact of existing approaches on each domain. We conclude by synthesizing the emerging directions for research in the specialized literature, which include novel representations for omnidirectional images, inter- and intra- image saliency decomposition for co-saliency, and saliency shift for video saliency estimation.

show abstract

Section: Methods For Co-saliencymentioning

confidence: 99%

Section: Methods For Co-saliencymentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

Buzzelli¹

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Learning-based methods [21], [34]- [37], [49]- [52] have attracted increasing attention since machine learning is much superior and achieves great success for co-saliency detection. Wei et al [35] extracted co-saliency correspondence with an end-to-end fully convolution network architecture.…”

Section: B Co-saliency Detectionmentioning

confidence: 99%

Co-Saliency Detection With Co-Attention Fully Convolutional Network

Gao

Zhao²,

Liu

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and achieve satisfactory detection results. However, due to stacking convolution layers and pooling operation, the boundary details tend to be lost. In addition, existing models often utilize the extracted features without discrimination, leading to redundancy in representation since actually not all features are helpful to the final prediction and some even bring distraction. In this paper, we propose a co-attention module embedded FCN framework, called as Co-Attention FCN (CA-FCN). Specifically, the co-attention module is plugged into the high-level convolution layers of FCN, which can assign larger attention weights on the common salient objects and smaller ones on the background and uncommon distractors to boost final detection performance. Extensive experiments on three popular co-saliency benchmark datasets demonstrate the superiority of the proposed CA-FCN, which outperforms state-of-the-arts in most cases. Besides, the effectiveness of our new co-attention module is also validated with ablation studies.

show abstract

“…Feature Vector Calculation: The texture feature is added to the KNN matting method, so in the HSV colour space, the feature vector of a given pixel p i can be expressed as a sevendimensional vector V(p i ). where x; y ð Þ is the spatial coordinate of pixel p i , and T is the texture feature obtained by Equation (14).…”

Section: The Linear Filter Response Of the Template At Pointmentioning

confidence: 99%

Consistent image processing based on co‐saliency

Ren

Hua

et al. 2021

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co-saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co-saliency detection. Through the co-saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non-common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing

Cited by 81 publications

References 46 publications

Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

Co-Saliency Detection With Co-Attention Fully Convolutional Network

Consistent image processing based on co‐saliency

Contact Info

Product

Resources

About