Fine-Grained Image Classification
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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Greatest papers with code
AutoAugment: Learning Augmentation Policies from Data
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.
SOTA for Image Classification on SVHN
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
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)
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.
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.
#2 best model for
Fine-Grained Image Classification
on FGVC Aircraft
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION
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)
Pairwise Confusion for Fine-Grained Visual Classification
ECCV 2018
• abhimanyudubey/confusion
• 
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
#2 best model for
Fine-Grained Image Classification
on NABirds
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
DATA AUGMENTATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE CROPPING
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
CVPR 2018
• richardaecn/cvpr18-inaturalist-transfer
• 
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.
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED VISUAL CATEGORIZATION TRANSFER LEARNING
Attribute-Aware Attention Model for Fine-grained Representation Learning
• iamhankai/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.
FINE-GRAINED IMAGE CLASSIFICATION IMAGE RETRIEVAL PERSON RE-IDENTIFICATION REPRESENTATION LEARNING
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
• HCPLab-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.
#5 best model for
Fine-Grained Image Classification
on CUB-200-2011
FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION REPRESENTATION LEARNING