A survey on deep learning-based fine-grained object classification and semantic segmentation
The deep learning technology has shown impressive performance in various vision tasks
such as image classification, object detection and semantic segmentation. In particular …
such as image classification, object detection and semantic segmentation. In particular …
[PDF][PDF] 基于深度卷积特征的细粒度图像分类研究综述
罗建豪， 吴建鑫 - 自动化学报, 2017 - aas.net.cn
摘要细粒度图像分类问题是计算机视觉领域一项极具挑战的研究课题, 其目标是对子类进行识别
, 如区分不同种类的鸟. 由于子类别间细微的类间差异和较大的类内差异, 传统的分类算法不得不 …
, 如区分不同种类的鸟. 由于子类别间细微的类间差异和较大的类内差异, 传统的分类算法不得不 …
This looks like that: deep learning for interpretable image recognition
When we are faced with challenging image classification tasks, we often explain our
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …
Deformable protopnet: An interpretable image classifier using deformable prototypes
We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable
image classifier that integrates the power of deep learning and the interpretability of case …
image classifier that integrates the power of deep learning and the interpretability of case …
Learning attentive pairwise interaction for fine-grained classification
Fine-grained classification is a challenging problem, due to subtle differences among highly-
confused categories. Most approaches address this difficulty by learning discriminative …
confused categories. Most approaches address this difficulty by learning discriminative …
Learning to navigate for fine-grained classification
Fine-grained classification is challenging due to the difficulty of finding discriminative
features. Finding those subtle traits that fully characterize the object is not straightforward. To …
features. Finding those subtle traits that fully characterize the object is not straightforward. To …
The devil is in the channels: Mutual-channel loss for fine-grained image classification
The key to solving fine-grained image categorization is finding discriminate and local
regions that correspond to subtle visual traits. Great strides have been made, with complex …
regions that correspond to subtle visual traits. Great strides have been made, with complex …
Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition
Learning subtle yet discriminative features (eg, beak and eyes for a bird) plays a significant
role in fine-grained image recognition. Existing attention-based approaches localize and …
role in fine-grained image recognition. Existing attention-based approaches localize and …
Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
Recognizing fine-grained categories (eg, bird species) is difficult due to the challenges of
discriminative region localization and fine-grained feature learning. Existing approaches …
discriminative region localization and fine-grained feature learning. Existing approaches …
Learning multi-attention convolutional neural network for fine-grained image recognition
Recognizing fine-grained categories (eg, bird species) highly relies on discriminative part
localization and part-based fine-grained feature learning. Existing approaches …
localization and part-based fine-grained feature learning. Existing approaches …