Fine-Grained Image Recognition
33 papers with code • 4 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Fine-Grained Image Recognition
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Datasets
CUB-200-2011
OmniBenchmark
OVEN
WebFG-496
CUB-GHA
Goldfinch
WikiChurches
CNFOOD-241-Chen
Most implemented papers
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
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
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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
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
JDAI-CV/DCL
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CVPR 2019
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
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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
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
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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.
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
hawkeye-finegrained/hawkeye
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However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field.
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
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
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.
