Галерея 3004641
Галерея 3004641
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Interesting yet challenging quiz I found on a website. My answer is a 1 √ 2 .
After I assumed the semicircle has radius r sin 45 , where r is the radius of the quarter circular part.
Let R be the radius of the outer circle and M = ( r , r ) be the center of the brown semidisc. Then | O M | = √ 2 r and therefore R 2 = 3 r 2 . The ratio of the areas then comes to 2 3 .
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Abstract: In this article, we focus on the task of zero-shot image classification (ZSIC) that equips a learning system with the ability to recognize visual images from unseen class... View more
In this article, we focus on the task of zero-shot image classification (ZSIC) that equips a learning system with the ability to recognize visual images from unseen classes. In contrast to the traditional image classification, ZSIC more easily suffers from the class-imbalance issue since it is more concerned with the class-level knowledge transferring capability. In the real world, the sample numbers of different categories generally follow a long-tailed distribution, and the discriminative information in the sample-scarce seen classes is hard to transfer to the related unseen classes in the traditional batch-based training manner, which degrades the overall generalization ability a lot. To alleviate the class-imbalance issue in ZSIC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model. Specifically, we randomly select the same number of images from each class across all training classes to form a training batch to ensure that the sample-scarce classes contribute equally as those classes with sufficient samples during each iteration. Considering that the instances from the same class differ in class representativeness, we further develop an efficient semantic-guided feature fusion model to obtain the discriminative class visual prototype for the following visual–semantic interaction process via distributing different weights to the selected samples based on their class representativeness. Extensive experiments on three imbalanced ZSIC benchmark datasets for both traditional ZSIC and generalized ZSIC tasks demonstrate that our approach achieves promising results, especially for the unseen categories that are closely related to the sample-scarce seen categories. Besides, the experimental results on two class-balanced datasets show that the proposed approach also improves the classification performance against the baseline model.
Published in: IEEE Transactions on Cybernetics ( Volume: 52 , Issue: 7 , July 2022 )
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Image classification has achieved remarkable success with the emergence of deep learning [1], [2], [6] and large-scale datasets [7]–[9]. However, the traditional supervised models are data-hungry approaches that require a large amount of well-labeled training data to feed them up. Besides, the traditional supervised models are unable to generalize to the new categories, that is, they are a closed classification setting, which violates the open-world characteristics to some extent.
2017 25th Signal Processing and Communications Applications Conference (SIU)
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
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Блондинка кайфует прислонив вибратор к клитору
Мари залетела от случайного знакомого 2
Секс фото американки Вероники Рашель