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Ground truth for saliency prediction datasets consists of two types of mapdata: fixation pixel map which records the human eye movements on sampleimages, and fixation blob map generated by performing gaussian blurring on thecorresponding fixation pixel map. Current saliency approaches performprediction by directly pixel-wise regressing the input image into saliency mapwith fixation blob as ground truth, yet learning saliency from fixation pixelmap is not explored. In this work, we propose a first-of-its-kind approach oflearning saliency prediction from sparse fixation pixel map, and a novel lossfunction for training from such sparse fixation. We utilize clustering toextract sparse fixation pixel from the raw fixation pixel map, and add amax-pooling transformation on the output to avoid false penalty between sparseoutputs and labels caused by nearby but non-overlapping saliency pixels whencalculating loss. This approach provides a novel perspective for achievingsaliency prediction. We evaluate our approach over multiple benchmark datasets,and achieve competitive performance in terms of multiple metrics comparing withstate-of-the-art saliency methods.

Learning Saliency Prediction From Sparse Fixation Pixel Map