Saliency Benchmark

MIT Saliency Benchmark Results: MIT300. The following are results of models evaluated on their ability to predict ground truth human fixations on our benchmark data set containing 300 natural images with eye tracking data from 39 observers. We post the results here and provide a way for people to submit new models for evaluation.

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References in zbMATH (referenced in 3 articles )

Showing results 1 to 3 of 3.
Sorted by year (citations)

  1. Galiano, G.; Ramírez, I.; Schiavi, E.: Non-convex non-local reactive flows for saliency detection and segmentation (2020)
  2. Calden Wloka, Toni Kunić, Iuliia Kotseruba, Ramin Fahimi, Nicholas Frosst, Neil D. B. Bruce, John K. Tsotsos: SMILER: Saliency Model Implementation Library for Experimental Research (2018) arXiv
  3. Srinivas S. S. Kruthiventi; Kumar Ayush; R. Venkatesh Babu: DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations (2015) arXiv