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Fine-Grained Image Classification

6 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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Greatest papers with code

AutoAugment: Learning Augmentation Policies from Data

24 May 2018tensorflow/models

Our key insight is to create a search space of data augmentation policies, evaluating the quality of a particular policy directly on the dataset of interest. For example, the policy learned on ImageNet allows us to achieve state-of-the-art accuracy on the fine grained visual classification dataset Stanford Cars, without fine-tuning weights pre-trained on additional data.

FINE-GRAINED IMAGE CLASSIFICATION

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. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance.

FINE-GRAINED IMAGE CLASSIFICATION

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

CVPR 2018 jiangtaoxie/fast-MPN-COV

Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks. Our method is much faster than EIG or SVD based ones, since it involves only matrix multiplications, suitable for parallel implementation on GPU.

FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION

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. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn finer-grained feature representation.

FINE-GRAINED IMAGE CLASSIFICATION FINE-GRAINED IMAGE RECOGNITION REPRESENTATION LEARNING

Attribute-Aware Attention Model for Fine-grained Representation Learning

2 Jan 2019iamhankai/attribute-aware-attention

Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. 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

A Large-Scale Car Dataset for Fine-Grained Categorization and Verification

CVPR 2015 seanren96/Object-Detection

Updated on 24/09/2015: This update provides preliminary experiment results for fine-grained classification on the surveillance data of CompCars. The train/test splits are provided in the updated dataset.

FINE-GRAINED IMAGE CLASSIFICATION