Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable
classification by associating predictions with a set of training prototypes, which we refer to as …

Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of
history, it is still limited to recognizing 2 images. Recognizing objects in the physical world …

Fine-grained classification often requires recognizing specific object parts, such as beak
shape and wing patterns for birds. Encouraging a fine-grained classification model to first …

Because the subtle differences between the different sub-categories of common visual
categories such as bird species, fine-grained classification has been seen as a challenging …

Existing fine-grained image recognition works have attempted to dig into low-level details for
emphasizing subtle discrepancies among sub-categories. However, a potential limitation of …

Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass
classification within the same large class using only label information. Compared to general …

This paper presents an effective method for strengthening the discriminative ability of high-
level deep features by enhancing and aggregating discriminative part-level features for the …

In the field of fine-grained food recognition, subset learning-based methods offer a strategic
approach that groups classes into subsets to guide the training process. Our study …

细粒度图像识别任务中, 在整体相似度极大而局部细节不同的图片中提取具有关注度的区域,
并对其中的特征加以学习是至关重要的任务. 针对目前研究中存在的人工标注判别区域的成本太 …

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is
essential for conducting biodiversity surveys. However, fine-grained image recognition of …