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.
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.
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.
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).
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