Fine-Grained Image Recognition

26 papers with code • 2 benchmarks • 7 datasets

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Most implemented papers

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

ZF1044404254/TBMSL-Net 20 Mar 2020

Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales.

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

jiangtaoxie/fast-MPN-COV CVPR 2018

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

Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition

Jianlong-Fu/Multi-Attention-CNN ICCV 2017

Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way.

Destruction and Construction Learning for Fine-Grained Image Recognition


In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge.

Local Patch AutoAugment with Multi-Agent Collaboration

LinShiqi047/PatchAutoAugment 20 Mar 2021

We formulate it as a multi-agent reinforcement learning (MARL) problem, where each agent learns an augmentation policy for each patch based on its content together with the semantics of the whole image.

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

deercoder/DeepFood 17 Jun 2016

We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results.

Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition

xcnkx/fine_grained_classification ECCV 2018

Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the correlations among them.

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

HCPLab-SYSU/HSE 14 Aug 2018

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.

Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition

researchmm/tasn CVPR 2019

Learning subtle yet discriminative features (e. g., beak and eyes for a bird) plays a significant role in fine-grained image recognition.

Deep Learning for Fine-Grained Image Analysis: A Survey

Sorananakiii/Fine-grained-image-recognition 6 Jul 2019

Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications.