Recent advances in convolutional neural networks
In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …
problems, such as visual recognition, speech recognition and natural language processing …
Learning to navigate for fine-grained classification
Fine-grained classification is challenging due to the difficulty of finding discriminative
features. Finding those subtle traits that fully characterize the object is not straightforward. To …
features. Finding those subtle traits that fully characterize the object is not straightforward. To …
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) …
Multi-attention multi-class constraint for fine-grained image recognition
Attention-based learning for fine-grained image recognition remains a challenging task,
where most of the existing methods treat each object part in isolation, while neglecting the …
where most of the existing methods treat each object part in isolation, while neglecting the …
Hierarchical bilinear pooling for fine-grained visual recognition
Fine-grained visual recognition is challenging because it highly relies on the modeling of
various semantic parts and fine-grained feature learning. Bilinear pooling based models …
various semantic parts and fine-grained feature learning. Bilinear pooling based models …
Learning a discriminative filter bank within a CNN for fine-grained recognition
Compared to earlier multistage frameworks using CNN features, recent end-to-end deep
approaches for fine-grained recognition essentially enhance the mid-level learning …
approaches for fine-grained recognition essentially enhance the mid-level learning …
Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization
Fine-grained image recognition is a challenging computer vision problem, due to the small
inter-class variations caused by highly similar subordinate categories, and the large intra …
inter-class variations caused by highly similar subordinate categories, and the large intra …
Deep learning of graph matching
A Zanfir, C Sminchisescu - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
The problem of graph matching under node and pair-wise constraints is fundamental in
areas as diverse as combinatorial optimization, machine learning or computer vision, where …
areas as diverse as combinatorial optimization, machine learning or computer vision, where …
Deep attention-based spatially recursive networks for fine-grained visual recognition
Fine-grained visual recognition is an important problem in pattern recognition applications.
However, it is a challenging task due to the subtle interclass difference and large intraclass …
However, it is a challenging task due to the subtle interclass difference and large intraclass …
A unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning
Prevalent techniques in zero-shot learning do not generalize well to other related problem
scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero …
scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero …