Fine-Grained Visual Recognition
35 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Fine-Grained Visual Recognition
Libraries
Use these libraries to find Fine-Grained Visual Recognition models and implementations
MTL-AQA
WHOI-Plankton
WebFG-496
FeathersV1
WikiChurches
Most implemented papers
Self-Paced Learning with Adaptive Deep Visual Embeddings
Selecting the most appropriate data examples to present a deep neural network (DNN) at different stages of training is an unsolved challenge.
Fine-grained visual recognition with salient feature detection
Computer vision based fine-grained recognition has received great attention in recent years.
The iMet Collection 2019 Challenge Dataset
Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification.
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
apple2373/MetaIRNet
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NeurIPS 2019
One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes.
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs).
Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
mboboGO/MOMN
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In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.
Interpretable and Accurate Fine-grained Recognition via Region Grouping
zxhuang1698/interpretability-by-parts
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CVPR 2020
Our results compare favorably to state-of-the-art methods on classification tasks, and our method outperforms previous approaches on the localization of object parts.
ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification
Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner.
Data-driven Meta-set Based Fine-Grained Visual Classification
NUST-Machine-Intelligence-Laboratory/dmbfgvr
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To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
NUST-Machine-Intelligence-Laboratory/Advanced-Softly-Update-Drop
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Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
