SwinFG: A fine-grained recognition scheme based on swin transformer
Z Ma, X Wu, A Chu, L Huang, Z Wei - Expert Systems with Applications, 2024 - Elsevier
Fine-grained image recognition (FGIR) is a challenging task as it requires the recognition of
sub-categories with subtle differences. Recently, the swin transformer has shown impressive …
sub-categories with subtle differences. Recently, the swin transformer has shown impressive …
Transfg: A transformer architecture for fine-grained recognition
Fine-grained visual classification (FGVC) which aims at recognizing objects from
subcategories is a very challenging task due to the inherently subtle inter-class differences …
subcategories is a very challenging task due to the inherently subtle inter-class differences …
TransIFC: Invariant cues-aware feature concentration learning for efficient fine-grained bird image classification
Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird
observation and protection but also a prevalent task for image classification in multimedia …
observation and protection but also a prevalent task for image classification in multimedia …
Denoising vision transformers
We study a crucial yet often overlooked issue inherent to Vision Transformers (ViTs): feature
maps of these models exhibit grid-like artifacts (“Original features” in Fig. 1), which hurt the …
maps of these models exhibit grid-like artifacts (“Original features” in Fig. 1), which hurt the …
[HTML][HTML] Towards a multisensor station for automated biodiversity monitoring
Rapid changes of the biosphere observed in recent years are caused by both small and
large scale drivers, like shifts in temperature, transformations in land-use, or changes in the …
large scale drivers, like shifts in temperature, transformations in land-use, or changes in the …
Fine-grained visual classification via internal ensemble learning transformer
Recently, vision transformers (ViTs) have been investigated in fine-grained visual
recognition (FGVC) and are now considered state of the art. However, most ViT-based works …
recognition (FGVC) and are now considered state of the art. However, most ViT-based works …
SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization
Over the past few years, a significant progress has been made in deep convolutional neural
networks (CNNs)-based image recognition. This is mainly due to the strong ability of such …
networks (CNNs)-based image recognition. This is mainly due to the strong ability of such …
Granularity-aware distillation and structure modeling region proposal network for fine-grained image classification
X Ke, Y Cai, B Chen, H Liu, W Guo - Pattern Recognition, 2023 - Elsevier
Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-
categories of the same super-category. The key to solving fine-grained classification …
categories of the same super-category. The key to solving fine-grained classification …
A novel plug-in module for fine-grained visual classification
Visual classification can be divided into coarse-grained and fine-grained classification.
Coarse-grained classification represents categories with a large degree of dissimilarity, such …
Coarse-grained classification represents categories with a large degree of dissimilarity, such …
Re-rank coarse classification with local region enhanced features for fine-grained image recognition
Fine-grained image recognition is very challenging due to the difficulty of capturing both
semantic global features and discriminative local features. Meanwhile, these two features …
semantic global features and discriminative local features. Meanwhile, these two features …