Fine-grained image classification is to recognize hundreds of subcategories belonging to
the same basic-level category, such as 200 subcategories belonging to the bird, which is …

Weakly supervised object localization remains a challenge when learning object localization
models from image category labels. Optimizing image classification tends to activate object …

We aim to divide the problem space of fine-grained recognition into some specific regions.
To achieve this, we develop a unified framework based on a mixture of experts. Due to …

We present a two-stage learning framework for weakly supervised object localization
(WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation …

Data mixing augmentation has proved effective in training deep models. Recent methods
mix labels mainly according to the mixture proportion of image pixels. Due to the major …

Deep neural networks have demonstrated advanced abilities on various visual classification
tasks, which heavily rely on the large-scale training samples with annotated ground-truth …

We propose a geometry constrained network, termed GC-Net, for weakly supervised object
localization (WSOL). GC-Net consists of three modules: a detector, a generator and a …

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 …

Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories that
belong to the same superclass. Since the distinctions among similar subcategories are quite …

Traditional feature encoding scheme (eg, Fisher vector) with local descriptors (eg, SIFT) and
recent convolutional neural networks (CNNs) are two classes of successful methods for …