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Visual vs internal attention mechanisms in deep neural networks for image classification and object detection.

, , , and . Pattern Recognit., (2022)

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A Comparative Study of Robust Segmentation Algorithms for Iris Verification System of High Reliability., , , , and . MCPR, volume 9116 of Lecture Notes in Computer Science, page 156-165. Springer, (2015)Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features., , , and . J. Electronic Imaging, 26 (1): 11016 (2017)Saliency-based selection of visual content for deep convolutional neural networks - Application to architectural style classification., , , and . Multim. Tools Appl., 78 (8): 9553-9576 (2019)Video Images Fusion to Improve Iris Recognition Accuracy in Unconstrained Environments., , , , and . MCPR, volume 7914 of Lecture Notes in Computer Science, page 114-125. Springer, (2013)Visual vs internal attention mechanisms in deep neural networks for image classification and object detection., , , and . Pattern Recognit., (2022)Semi-Automatic Annotation with Predicted Visual Saliency Maps for Object Recognition in Wearable Video., , , and . WearMMe@ICMR, page 10-14. ACM, (2017)Iris Segmentation Using a Statistical Approach., , and . MCPR, volume 6256 of Lecture Notes in Computer Science, page 164-170. Springer, (2010)Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs., , , , , and . IPTA, page 1-6. IEEE, (2018)Optimized robust multi-sensor scheme for simultaneous video and image iris recognition., , , , and . Pattern Recognit. Lett., (2018)Adaptive Spatial Concealment of Damaged Coded Images., , and . MCPR, volume 7329 of Lecture Notes in Computer Science, page 94-106. Springer, (2012)