Browse > Computer Vision > Image Classification > Fine-Grained Image Classification

# Fine-Grained Image Classification Edit

13 papers with code · Computer Vision

The Fine-Grained Image Classification task focuses on differentiating between hard-to-distinguish object classes, such as species of birds, flowers, or animals; and identifying the makes or models of vehicles.

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# AutoAugment: Learning Augmentation Policies from Data

24 May 2018tensorflow/models

In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.

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# GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

16 Nov 2018tensorflow/lingvo

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.

SOTA for Image Classification on CIFAR-10 (using extra training data)

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# Learning to Navigate for Fine-grained Classification

In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher.

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# Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks.

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# Fixing the train-test resolution discrepancy

Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86. 4% (top-5: 98. 0%) (single-crop).

SOTA for Image Classification on ImageNet (using extra training data)

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# Pairwise Confusion for Fine-Grained Visual Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.

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# See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

26 Jan 2019GuYuc/WS-DAN.PyTorch

Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.

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# Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.

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# Attribute-Aware Attention Model for Fine-grained Representation Learning

2 Jan 2019iamhankai/attribute-aware-attention

Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner.

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# Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding

14 Aug 2018HCPLab-SYSU/HSE

In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework.

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