Papers with Code 아카이브

Visual Recognition

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ChaojianYu/Hierarchical-Bilinear-Pooling

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Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

https://github.com/KeremTurgutlu/clip_art

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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

https://github.com/KeremTurgutlu/self_supervised

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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

https://github.com/KingJamesSong/DifferentiableSVD

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On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition

https://github.com/NUST-Machine-Intelligence-Laboratory/Advanced-Softly-Update-Drop

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Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

https://github.com/NUST-Machine-Intelligence-Laboratory/dmbfgvr

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Data-driven Meta-set Based Fine-Grained Visual Classification

https://github.com/Panda-Peter/image-captioning

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X-Linear Attention Networks for Image Captioning

https://github.com/ViLab-UCSD/MemSAC_ECCV2022

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MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation

https://github.com/ZhangLi-CS/GCP_Optimization

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Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

https://github.com/alanlusun/few-shot-keypoint-detection

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Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species

https://github.com/apple2373/MetaIRNet

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Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

https://github.com/avichapman/feature-magnitude-regularization

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Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization

https://github.com/clovaai/assembled-cnn

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Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

https://github.com/demidovd98/fgic_lowd

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Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes

https://github.com/faresbougourzi/ccfg-net

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Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wild

https://github.com/feathers-dataset/feathersv1-classification

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Feathers dataset for Fine-Grained Visual Categorization

https://github.com/feathers-dataset/feathersv1-dataset

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Feathers dataset for Fine-Grained Visual Categorization

https://github.com/ganperf/lcr

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Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems

https://github.com/ganperf/sam

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Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism

https://github.com/gofynd/mildnet

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MILDNet: A Lightweight Single Scaled Deep Ranking Architecture

https://github.com/icvteam/part

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Part-guided Relational Transformers for Fine-grained Visual Recognition

https://github.com/jiangtaoxie/MPN-COV

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Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

https://github.com/jiangtaoxie/fast-MPN-COV

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Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

https://github.com/liuziyu77/rar

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RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition

https://github.com/mboboGO/MOMN

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Multi-Objective Matrix Normalization for Fine-grained Visual Recognition

https://github.com/pku-icst-mipl/finedefics_iclr2025

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Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models

https://github.com/raoyongming/CAL

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Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

https://github.com/richard-peng-xia/HGCLIP

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HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding

https://github.com/sgvaze/generalized-category-discovery

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Generalized Category Discovery

https://github.com/soumitri2001/gcpl

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Towards Generative Class Prompt Learning for Fine-grained Visual Recognition

https://github.com/sycny/selfsynthx

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Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data

https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/fashionpedia

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Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

https://github.com/tpcd/dccl

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Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

https://github.com/tttyuntian/vlm_primitive_concepts

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Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?

https://github.com/vithursant/SPL-ADVisE

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Self-Paced Learning with Adaptive Deep Visual Embeddings

https://github.com/wuyun8210/partdetection

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Fine-grained visual recognition with salient feature detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.