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 …
Destruction and construction learning for fine-grained image recognition
Delicate feature representation about object parts plays a critical role in fine-grained
recognition. For example, experts can even distinguish fine-grained objects relying only on …
recognition. For example, experts can even distinguish fine-grained objects relying only on …
Learning deep bilinear transformation for fine-grained image representation
Bilinear feature transformation has shown the state-of-the-art performance in learning fine-
grained image representations. However, the computational cost to learn pairwise …
grained image representations. However, the computational cost to learn pairwise …
Scops: Self-supervised co-part segmentation
Parts provide a good intermediate representation of objects that is robust with respect to
camera, pose and appearance variations. Existing work on part segmentation is dominated …
camera, pose and appearance variations. Existing work on part segmentation is dominated …
Fine-grained vehicle classification with channel max pooling modified CNNs
Convolutional neural networks (CNNs) have recently shown excellent performance on the
task of fine-grained vehicle classification, where the motivation is to identify the fine-grained …
task of fine-grained vehicle classification, where the motivation is to identify the fine-grained …
P-CNN: Part-based convolutional neural networks for fine-grained visual categorization
This paper proposes an end-to-end fine-grained visual categorization system, termed Part-
based Convolutional Neural Network (P-CNN), which consists of three modules. The first …
based Convolutional Neural Network (P-CNN), which consists of three modules. The first …
Learning rich part hierarchies with progressive attention networks for fine-grained image recognition
We investigate the localization of subtle yet discriminative parts for fine-grained image
recognition. Based on the observation that such parts typically exist within a hierarchical …
recognition. Based on the observation that such parts typically exist within a hierarchical …
Object detection from scratch with deep supervision
In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection
framework that can be trained from scratch. Recent advances in object detection heavily …
framework that can be trained from scratch. Recent advances in object detection heavily …
Which and how many regions to gaze: Focus discriminative regions for fine-grained visual categorization
Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories that
belong to the same superclass. Since the distinctions among similar subcategories are quite …
belong to the same superclass. Since the distinctions among similar subcategories are quite …
Convolutional low-resolution fine-grained classification
Successful fine-grained image classification methods learn subtle details between visually
similar (sub-) classes, but the problem becomes significantly more challenging if the details …
similar (sub-) classes, but the problem becomes significantly more challenging if the details …