Top-Down Saliency Detection Driven by Visual Classification
This work addresses the challenge of improving saliency detection and visual classification for applications in computer vision, though it appears incremental by building on existing CNN frameworks.
The paper tackles the problem of saliency detection by proposing a top-down approach guided by visual classification tasks, showing that SalClassNet outperforms state-of-the-art saliency detectors like SalNet and SALICON and enhances classification accuracy in fine-grained recognition.
This paper presents an approach for top-down saliency detection guided by visual classification tasks. We first learn how to compute visual saliency when a specific visual task has to be accomplished, as opposed to most state-of-the-art methods which assess saliency merely through bottom-up principles. Afterwards, we investigate if and to what extent visual saliency can support visual classification in nontrivial cases. To achieve this, we propose SalClassNet, a CNN framework consisting of two networks jointly trained: a) the first one computing top-down saliency maps from input images, and b) the second one exploiting the computed saliency maps for visual classification. To test our approach, we collected a dataset of eye-gaze maps, using a Tobii T60 eye tracker, by asking several subjects to look at images from the Stanford Dogs dataset, with the objective of distinguishing dog breeds. Performance analysis on our dataset and other saliency bench-marking datasets, such as POET, showed that SalClassNet out-performs state-of-the-art saliency detectors, such as SalNet and SALICON. Finally, we analyzed the performance of SalClassNet in a fine-grained recognition task and found out that it generalizes better than existing visual classifiers. The achieved results, thus, demonstrate that 1) conditioning saliency detectors with object classes reaches state-of-the-art performance, and 2) providing explicitly top-down saliency maps to visual classifiers enhances classification accuracy.