Fine-Grained Image Recognition

31 papers with code • 3 benchmarks • 8 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.

Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities

edchengg/oven_eval ICCV 2023

Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong generalization on various visual domains and tasks.

PaLI-X: On Scaling up a Multilingual Vision and Language Model

kyegomez/PALI 29 May 2023

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture.

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.

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples

msfuxian/DualAttentionNet 11 May 2018

To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task.

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.