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Fine-Grained Image Recognition

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Showing 26–50 of 72 papers

TitleStatusHype
Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition—0
Retrieval-Enhanced Contrastive Vision-Text Models—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Application of attention-based Siamese composite neural network in medical image recognition—0
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets—0
Siamese transformer with hierarchical concept embedding for fine-grained image recognition—0
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition—0
PPL: Pairwise Prototype Learning for Masked Face RecognitionCode0
Learning Deep Optimal Embeddings with Sinkhorn Divergences—0
Taxonomy and evolution predicting using deep learning in imagesCode0
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation—0
Full-attention based Neural Architecture Search using Context Auto-regression—0
High-Order-Interaction for weakly supervised Fine-Grained Visual CategorizationCode0
Fine-Grained Image Analysis with Deep Learning: A Survey—0
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image RecognitionCode0
Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand HygieneCode0
Learning Canonical 3D Object Representation for Fine-Grained Recognition—0
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition—0
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition—0
Exploring Localization for Self-supervised Fine-grained Contrastive LearningCode0
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition—0
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition—0
Category-specific Semantic Coherency Learning for Fine-grained Image Recognition—0
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image RecognitionCode0
Volumetric Transformer Networks—0
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