T-NNLS 2015
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Tianshui Chen, Liang Lin*, Lingbo Liu, Xiaonan Luo, and Xuelong Li
T-NNLS 2015

Abstract


Architecture
Fig. Illustration of our proposed deep saliency computing model. The first CNN takes the whole image data as input and produces coarse map. Guided by the coarse map, the second CNN takes a local patch as input and generates the fine-grained saliency map.

 

 

Quantitative Comparisons


Quantitative_Comparisons_MSRA-10k
Fig. Experimental results on the MSRA10K dataset. (a) Precision-recall curve, (b) precision-recall bar with F-measure, and (c) mean absolute error for comparing our model against previous works.

Quantitative_Comparisons_SED1_ECSSD_PASCAL1500
Fig. Experimental results on the (a) SED1, (b) ECSSD, and (c) PASCAL1500 datasets compared with previous works. Precision-recall curves (the first row), precision-recall bar with F-measure (the second row), and mean absolute error (the third row) show superior generalization ability of our proposed method. Note that our method still achieves state-of-the-art performance even though the model is learned on MSRA10K without fine-tuning to the target datasets.

Quantitative_Comparision_with_CNN-based_Methods
Fig. The Precision-Recall with F-measure, MEA and Running Time of DISC and other three CNN-base methods on SED1 dataset.

 

 

Visual Comparisons


Visual_Comparisons
Fig. Visual comparision with previous methods. The images are taken from MSRA10K (first two columns), SED1 (third and fourth columns), ECSSD (fifth and sixth columns), and PASCAL1500 (last two columms). Our results not only highlight the overall objects but preserve boundary and structure details.

 

 

Task-Oriented Adaptation


Task-Oriented_Adaptation
Fig. The result of task-oriented salient object detection without and with retraining. All the salient objects are highlighted in (d), but only the specific object is highlighted after retraining in (c).

 

 

 

References


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