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computer-vision-11

Fine-Grained Visual Categorization

3260 papers • 126 benchmarks • 313 datasets

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Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

Fan Zhang, Meng Li, G. Zhai, Yizhao Liu•Thu Mar 19 2020

Through the comprehensive experiments demonstrate that the multi-branch and multi-scale learning network, MMAL-Net, has good classification ability and robustness for images of different scales and can achieves state-of-the-art results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets.

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The Plant Pathology 2020 challenge dataset to classify foliar disease of apples

Serge J. Belongie, Noah Snavely, Ranjita Thapa, Awais Khan Plant Pathology, Plant-Microbe Biology Section, Cornell University, Geneva, Ny, Cornell Tech•Thu Apr 23 2020

A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for 'Plant Pathology Challenge'; part of the Fine-Grained Visual Categorization workshop at CVPR 2020.

75 0

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Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

Yin Cui, Serge J. Belongie, Hartwig Adam, B. Hariharan, Claire Cardie, Menglin Jia, Mengyun Shi, Mikhail Sirotenko•Sat Apr 25 2020

This work proposes a novel Attribute-Mask RCNN model to jointly perform instance segmentation and localized attribute recognition, and provides a novel evaluation metric for the task.

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Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

K. Jia, Yabin Zhang, Hui Tang•Fri Jul 27 2018

A new deep FGVC model termed MetaFGNet is proposed, based on a novel regularized meta-learning objective, which aims to guide the learning of network parameters so that they are optimal for adapting to the target FGVC task.

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Classification-Specific Parts for Improving Fine-Grained Visual Categorization

Joachim Denzler, D. Korsch, P. Bodesheim•Mon Sep 09 2019

This work proposes a novel classification-specific part estimation that uses an initial prediction as well as back-propagation of feature importance via gradient computations in order to estimate relevant image regions and shows the effectiveness of the mentioned part selection method in conjunction with the extracted part features.

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Feathers dataset for fine-grained visual categorization

A. Belko, K. Dobratulin, A. Kuznetsov•Fri Apr 17 2020

A novel dataset FeatherV1, containing 28,272 images of feathers categorized by 595 bird species, was created to perform taxonomic identification of bird species by a single feather, which can be applied in amateur and professional ornithology.

3 0

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Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Longyin Wen, Dawei Du, Libo Zhang, Xianglong Liu, Feiyue Huang, Chen Zhao, Ruyi Ji, Ynajun Wu•Tue Sep 24 2019

An attention convolutional binary neural tree architecture is presented to address problems for weakly supervised Fine-grained visual categorization and uses the attention transformer module to enforce the network to capture discriminative features.

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Fine-Grained Visual-Textual Representation Learning

Xiangteng He, Yuxin Peng•Wed Aug 30 2017

A fine-grained visual-textual representation learning (VTRL) approach, which achieves the best categorization accuracy compared with the state-of-the-art methods.

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Fine-Grained Visual Categorization | State-of-the-Art