Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

@inproceedings{Yu2018HierarchicalBP,
  title={Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition},
  author={Chaojian Yu and Xinyi Zhao and Qi Zheng and Peng Zhang and Xinge You},
  booktitle={ECCV},
  year={2018}
}
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 have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross… Expand
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References

SHOWING 1-10 OF 39 REFERENCES
Bilinear CNN Models for Fine-Grained Visual Recognition
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain anExpand
Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition
TLDR
It is shown that zooming in on the selected attention regions significantly improves the performance of fine-grained recognition, and the proposed approach is noticeably more computationally efficient during both training and testing because of its fully-convolutional architecture. Expand
Compact Bilinear Pooling
  • Yang Gao, Oscar Beijbom, Ning Zhang, Trevor Darrell
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
Two compact bilinear representations are proposed with the same discriminative power as the full bil inear representation but with only a few thousand dimensions allowing back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. Expand
Fully Convolutional Attention Networks for Fine-Grained Recognition
TLDR
Fully Convolutional Attention Networks are introduced, a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine-grained domains and the greedy reward strategy accelerates the convergence of the learning. Expand
Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
TLDR
This paper proposes a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other, and shows the best performances on three challenging published fine-grained datasets. Expand
Low-Rank Bilinear Pooling for Fine-Grained Classification
TLDR
This work proposes a classifier co-decomposition that factorizes the collection of bilinear classifiers into a common factor and compact per-class terms and achieves state-of-the-art performance on several public datasets for fine-grained classification trained with only category labels. Expand
Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization
TLDR
This work proposes an end-to-end framework based on higherorder integration of hierarchical convolutional activations for FGVC that yields more discriminative representation and achieves competitive results on the widely used FGVC datasets. Expand
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition
TLDR
A novel recurrent attention convolutional neural network (RA-CNN) which recursively learns discriminative region attention and region-based feature representation at multiple scales in a mutual reinforced way and achieves the best performance in three fine-grained tasks. Expand
Multiple Granularity Descriptors for Fine-Grained Categorization
TLDR
This work leverages the fact that a subordinate-level object already has other labels in its ontology tree to train a series of CNN-based classifiers, each specialized at one grain level, which outperforms state-of-the-art algorithms, including those requiring strong labels. Expand
Kernel Pooling for Convolutional Neural Networks
TLDR
This work demonstrates how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner and proposes a general pooling framework that captures higher order interactions of features in the form of kernels. Expand
...
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3
4
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