Fine-grained image analysis with deep learning: A survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
Adaptive adversarial network for source-free domain adaptation
Abstract Unsupervised Domain Adaptation solves knowledge transfer along with the
coexistence of well-annotated source domain and unlabeled target instances. However, the …
coexistence of well-annotated source domain and unlabeled target instances. However, the …
Large scale fine-grained categorization and domain-specific transfer learning
Transferring the knowledge learned from large scale datasets (eg, ImageNet) via fine-tuning
offers an effective solution for domain-specific fine-grained visual categorization (FGVC) …
offers an effective solution for domain-specific fine-grained visual categorization (FGVC) …
Object-part attention model for fine-grained image classification
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 …
the same basic-level category, such as 200 subcategories belonging to the bird, which is …
Learning a mixture of granularity-specific experts for fine-grained categorization
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 …
To achieve this, we develop a unified framework based on a mixture of experts. Due to …
Domain adaptation with neural embedding matching
Domain adaptation aims to exploit the supervision knowledge in a source domain for
learning prediction models in a target domain. In this article, we propose a novel …
learning prediction models in a target domain. In this article, we propose a novel …
A coarse-fine network for keypoint localization
We propose a coarse-fine network (CFN) that exploits multi-level supervisions for keypoint
localization. Recently, convolutional neural networks (CNNs)-based methods have achieved …
localization. Recently, convolutional neural networks (CNNs)-based methods have achieved …
Automated analysis of unregistered multi-view mammograms with deep learning
G Carneiro, J Nascimento… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC)
and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk …
and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk …
Fine-grained recognition with learnable semantic data augmentation
Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …
differentiating objects belonging to multiple subordinate categories within the same meta …
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 …