Analysis of Scores, Datasets, and Models in Visual Saliency Prediction
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Citations
Understanding More About Human and Machine Attention in Deep Neural Networks
A Unified Framework for Salient Structure Detection by Contour-Guided Visual Search
Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency
Predicting Human Saccadic Scanpaths Based on Iterative Representation Learning
Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challenges
References
A general method applicable to the search for similarities in the amino acid sequence of two proteins
Mean shift: a robust approach toward feature space analysis
A feature-integration theory of attention
A model of saliency-based visual attention for rapid scene analysis
A model of saliency-based visual attention for rapid scene analysis
Related Papers (5)
Frequently Asked Questions (11)
Q2. What future works have the authors mentioned in the paper "Analysis of scores, datasets, and models in visual saliency prediction" ?
The authors found that some stimulus categories are harder for models ( e. g., nature, nude, and portrait ) which warrant more attention in future works. Future directions: In this regard, it will also be interesting to test the feasibility of predicting whether a scene is natural or man-made from saliency and fixations. The authors believe it is important to constantly measure the gap between the IO model and models to find out in which directions models lag behind human performance.
Q3. What are the common types of stimuli used in neurophysiological and modeling works?
Visual stimuli used in neurophysiological and modeling works include: static (synthetic search arrays involving pop-out and conjunction search arrays, cartoons, or photographs) and over spatio-temporal dynamic stimuli (movies and interactive video games).
Q4. What are the two main causes of CB?
Two important causes for CB are: (1) Viewing strategy where subjects start looking from the image center and (2) A perhaps stronger, photographer bias, which is the tendency of photographers to frame interesting objects at the center.
Q5. What is the difficult challenge in the fixation datasets?
A difficult challenge in fixation datasets which has affected fair model comparison is “Center-Bias (CB)”, whereby humans often appear to preferentially look near an image’s center [28].
Q6. Why are there still inconsistencies in the results of previous benchmarks?
But due to a lack of an exhaustive coherent benchmarking system, to address several issues such as evaluation measures (e.g., at least 4 types of AUC measures have been used; supplement), center-bias, map characteristics (e.g., smoothing), and dataset bias, a lot of inconsistencies still exist.
Q7. What is the reason for the lack of models to predict scanpath sequence?
In the context of saliency modeling, few models have aimed to predict scanpath sequence, partly due to difficulty in measuringand quantizing scanpaths.
Q8. How do the authors make a fixation histogram?
Fixation histogram is made by dividing the image into a grid pattern (16 × 16) and counting the number of fixations in each grid.
Q9. What is the way to compute the histograms for a given image?
To compute the histograms for a given image, the authors initially compute corresponding features (e.g., saccade velocity, etc.) for each observer and quantize the values into several bins.
Q10. Why do the authors believe it is important to measure the gap between the IO model and models?
The authors believe it is important to constantly measure the gap between the IO model and models to find out in which directions models lag behind human performance.
Q11. What is the way to tune the parameters in a model?
Properly tuning these parameters is important in fair model comparison and is perhaps best left for a model developer to optimize himself.