Fine-Grained Image Classification

171 papers with code • 35 benchmarks • 36 datasets

Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.

( Image credit: Looking for the Devil in the Details )

Most implemented papers

DINOv2: Learning Robust Visual Features without Supervision

facebookresearch/dinov2 14 Apr 2023

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.

Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

layumi/Person-reID_GAN ICCV 2017

We verify the proposed method on a practical problem: person re-identification (re-ID).

Big Transfer (BiT): General Visual Representation Learning

google-research/big_transfer ECCV 2020

We conduct detailed analysis of the main components that lead to high transfer performance.

Escaping the Big Data Paradigm with Compact Transformers

SHI-Labs/Compact-Transformers 12 Apr 2021

Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.

Gradient Centralization: A New Optimization Technique for Deep Neural Networks

Yonghongwei/Gradient-Centralization ECCV 2020

It has been shown that using the first and second order statistics (e. g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance.

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.

Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches

PRIS-CV/PMG-Progressive-Multi-Granularity-Training ECCV 2020

In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.

Three things everyone should know about Vision Transformers

facebookresearch/deit 18 Mar 2022

(2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to a higher resolution and to other classification tasks.

Bilinear CNNs for Fine-grained Visual Recognition

tommarvoloriddle/Bilinear-CNN-Tensorflow2.4-implementation 29 Apr 2015

We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.

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.