Deep representation learning with part loss for person re-identification

H Yao, S Zhang, R Hong, Y Zhang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Learning discriminative representations for unseen person images is critical for person re-
identification (ReID). Most of the current approaches learn deep representations in …

Class attention network for image recognition

G Cheng, P Lai, D Gao, J Han - Science China Information Sciences, 2023 - Springer
Visual attention has become a popular and widely used component for image recognition.
Although various attention-based methods have been proposed and achieved relatively …

Domain-aware visual bias eliminating for generalized zero-shot learning

S Min, H Yao, H Xie, C Wang… - Proceedings of the …, 2020 - openaccess.thecvf.com
Generalized zero-shot learning aims to recognize images from seen and unseen domains.
Recent methods focus on learning a unified semantic-aligned visual representation to …

Accurate fine-grained object recognition with structure-driven relation graph networks

S Wang, Z Wang, H Li, J Chang, W Ouyang… - International Journal of …, 2024 - Springer
Fine-grained object recognition (FGOR) aims to learn discriminative features that can
identify the subtle distinctions between visually similar objects. However, less effort has …

SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization

A Bera, Z Wharton, Y Liu, N Bessis… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

Graph-propagation based correlation learning for weakly supervised fine-grained image classification

Z Wang, S Wang, H Li, Z Dou, J Li - Proceedings of the AAAI conference on …, 2020 - aaai.org
Abstract The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to
pick out the discriminative regions and learn the discriminative features from them. However …

P-CNN: Part-based convolutional neural networks for fine-grained visual categorization

J Han, X Yao, G Cheng, X Feng… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …

Weakly supervised fine-grained image classification via guassian mixture model oriented discriminative learning

Z Wang, S Wang, S Yang, H Li… - Proceedings of the …, 2020 - openaccess.thecvf.com
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick
out the discriminative regions from the high-level feature maps directly. We discover that due …

A unified matrix-based convolutional neural network for fine-grained image classification of wheat leaf diseases

Z Lin, S Mu, F Huang, KA Mateen, M Wang… - IEEE …, 2019 - ieeexplore.ieee.org
Fine-grained image classification methods often suffer from the challenge that the
subordinate categories within an entry-level category can only be distinguished by subtle …

Multi-objective matrix normalization for fine-grained visual recognition

S Min, H Yao, H Xie, ZJ Zha… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent
methods have shown that the matrix power normalization can stabilize the second-order …