https://github.com/HCPLab-SYSU/HSE
공식 코드Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Papers with Code 아카이브
Fine-Grained Image Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LeapLabTHU/LearnableISDA
공식 코드Fine-grained Recognition with Learnable Semantic Data Augmentation
https://github.com/LinShiqi047/PatchAutoAugment
공식 코드https://github.com/M-Nauta/ProtoTree
공식 코드Neural Prototype Trees for Interpretable Fine-grained Image Recognition
https://github.com/Westlake-AI/openmixup
공식 코드Exploring Localization for Self-supervised Fine-grained Contrastive Learning
https://github.com/ZF1044404254/MMAL-Net
공식 코드Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/ZF1044404254/TBMSL-Net
공식 코드Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/arkel23/DownSamplingInterLayerAdapter
공식 코드https://github.com/arkel23/GLSim
공식 코드Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers
https://github.com/arkel23/clca
공식 코드Cross-Layer Cache Aggregation for Token Reduction in Ultra-Fine-Grained Image Recognition
https://github.com/avichapman/feature-magnitude-regularization
공식 코드https://github.com/chishengchen/resvmamba
공식 코드https://github.com/chou141253/fgvc-pim
공식 코드A Novel Plug-in Module for Fine-Grained Visual Classification
https://github.com/edchengg/oven_eval
공식 코드Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
https://github.com/facebookresearch/visual-counterfactuals
공식 코드Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals
https://github.com/hawkeye-finegrained/hawkeye
공식 코드Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
https://github.com/jiangtaoxie/fast-MPN-COV
공식 코드https://github.com/linusericsson/ssl-transfer
공식 코드https://github.com/picekl/DanishFungiDataset
공식 코드Danish Fungi 2020 -- Not Just Another Image Recognition Dataset
https://github.com/prlz77/attend-and-rectify
공식 코드Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
https://github.com/raoyongming/CAL
공식 코드Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
https://github.com/researchmm/DBTNet
공식 코드Learning Deep Bilinear Transformation for Fine-grained Image Representation
https://github.com/researchmm/tasn
공식 코드https://github.com/set-path/multi-input-mushroom-recognition
공식 코드Taxonomy and evolution predicting using deep learning in images
https://github.com/subhc/clever
공식 코드The Curious Layperson: Fine-Grained Image Recognition without Expert Labels
https://github.com/usuyama/ePillID-benchmark
공식 코드ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification
https://github.com/willogy-team/hand-gesture-recognition-smc2021
공식 코드Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
https://github.com/1170500804/tbmsl
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/DanielKovach/AG-Net
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
https://github.com/Kso-Ayaka/MS_Paperwithcode/tree/main/Hawkeye
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
https://github.com/OrkhanHI/Grab-AI-Computer-Vision-Challenge
https://github.com/PaddlePaddle/PaddleClas
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
https://github.com/Sorananakiii/Fine-grained-image-recognition
https://github.com/ZF4444/MMAL-Net
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/deercoder/DeepFood
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
https://github.com/doc-doc/NExT-OE
PaLI-X: On Scaling up a Multilingual Vision and Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.