Leveraging the Wisdom of the Crowd for Fine-Grained Recognition
Leveraging the Wisdom of the Crowd for Fine-Grained Recognition
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DOI：
10.1109/tpami.2015.2439285
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发表时间：
2016-04-01
影响因子：
23.6
通讯作者：
Fei-Fei, Li
中科院分区：
文献类型：
--
作者：
Deng, Jia;Krause, Jonathan;Fei-Fei, Li
细粒度识别涉及下级的分类，其中对象类之间的区别是高度局部的。与基础级别的识别相比，细粒度的分类可能更具挑战性，因为通常数据较少，区分特征也较少。这需要使用更强的先验来进行特征选择。在这项工作中，我们将人类纳入循环中，以帮助计算机选择判别性特征。我们介绍了一款名为“Bubbles”的新颖在线游戏，它揭示了人类使用的歧视性特征。玩家的目标是识别严重模糊图像的类别。在游戏过程中，玩家可以选择显示圆形区域（“气泡”）的完整细节，但会受到一定的惩罚。通过正确的设置，游戏会生成具有保证质量的有辨别力的气泡。接下来，我们提出“BubbleBank”表示，它使用人类选择的气泡来提高机器识别性能。最后，我们演示如何将 BubbleBank 扩展到视图不变的 3D 表示。实验表明，我们的方法在具有挑战性的基准测试上比以前的技术水平有了很大的改进。
Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. This necessitates the use of a stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. We introduce a novel online game called "Bubbles" that reveals discriminative features humans use. The player's goal is to identify the category of a heavily blurred image. During the game, the player can choose to reveal full details of circular regions ("bubbles"), with a certain penalty. With proper setup the game generates discriminative bubbles with assured quality. We next propose the "BubbleBank" representation that uses the human selected bubbles to improve machine recognition performance. Finally, we demonstrate how to extend BubbleBank to a view-invariant 3D representation. Experiments demonstrate that our approach yields large improvements over the previous state of the art on challenging benchmarks.