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
Latest papers with no code
GCAM: Gaussian and causal-attention model of food fine-grained recognition
Currently, most food recognition relies on deep learning for category classification.
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition
Trained through an end-to-end multi-task learning process, this method enhances performance in the fine-grained food recognition task, showing exceptional prowess with highly similar classes.
Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition
Open-set image recognition is a challenging topic in computer vision.
Retrieval-Enhanced Contrastive Vision-Text Models
Contrastive image-text models such as CLIP form the building blocks of many state-of-the-art systems.
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification
Fine-grained classification poses greater challenges compared to basic-level image classification due to the visually similar sub-species.
Application of attention-based Siamese composite neural network in medical image recognition
Aiming at the problem of few-shot samples, a Siamese neural network suitable for classification model is proposed.
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets
Extensive experimentation shows improvements in the SoTA FGVR benchmarks of up to +1. 3% of accuracy using both CNNs and transformer-based networks.
Siamese transformer with hierarchical concept embedding for fine-grained image recognition
In particular, one subnetwork is for coarse-scale patches to learn the discriminative regions with the aid of the innate multi-head self-attention mechanism of the transformer.
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition
To address this problem, motivated by the temporal attention mechanism in brains, we propose a spatial-temporal attention network for learning fine-grained feature representations, called STAN, where the features learnt by implementing a sequence of spatial self-attention operations corresponding to multiple moments are aggregated progressively.
Learning Deep Optimal Embeddings with Sinkhorn Divergences
Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data.
