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
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks.
Full-attention based Neural Architecture Search using Context Auto-regression
Thus, it is appropriate to consider using NAS methods to discover a better self-attention architecture automatically.
Fine-Grained Image Analysis with Deep Learning: A Survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism.
Learning Canonical 3D Object Representation for Fine-Grained Recognition
By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object and achieves competitive performance on fine-grained image recognition and vehicle re-identification.
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
We propose the recurrent attention multi-scale transformer (RAMS-Trans), which uses the transformer's self-attention to recursively learn discriminative region attention in a multi-scale manner.
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition
In this paper, we analyze the difficulties of fine-grained image recognition from a new perspective and propose a transformer architecture with the peak suppression module and knowledge guidance module, which respects the diversification of discriminative features in a single image and the aggregation of discriminative clues among multiple images.
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
In this paper, a retrieval-based coarse-to-fine framework is proposed, where we re-rank the TopN classification results by using the local region enhanced embedding features to improve the Top1 accuracy (based on the observation that the correct category usually resides in TopN results).
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition
Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature.
Category-specific Semantic Coherency Learning for Fine-grained Image Recognition
Existing deep learning based weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature (HLF) maps directly.
