Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)
☆132Aug 27, 2020Updated 6 years ago
Alternatives and similar repositories for interpretability-by-parts
Users that are interested in interpretability-by-parts are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Code release for Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV2020)☆222Jun 8, 2021Updated 5 years ago
- ☆86Jan 13, 2021Updated 5 years ago
- Pytorch version of model in NeurIPS'19 paper "Learning Deep Bilinear Transformation for Fine-grained Image Representation"☆21Dec 29, 2019Updated 6 years ago
- This is the PyTorch implementation of our paper "Cross-X learning for Fine-Grained Visual Categorization"☆74Dec 4, 2019Updated 6 years ago
- Code for our NeurIPS'19 paper "Learning Deep Bilinear Transformation for Fine-grained Image Representation"☆105Jan 20, 2020Updated 6 years ago
- Managed hosting for WordPress and PHP on Cloudways • AdManaged hosting for WordPress, Magento, Laravel, or PHP apps, on multiple cloud providers. Deploy in minutes on Cloudways by DigitalOcean.
- Learning Attentive Pairwise Interaction for Fine-Grained Classification, AAAI-2020☆133Dec 26, 2021Updated 4 years ago
- Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)☆262Jul 29, 2022Updated 4 years ago
- (CVPR 2020) Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition☆40May 27, 2021Updated 5 years ago
- Trilinear Attention Sampling Network for Fine-grained Image Recognition☆219Dec 14, 2021Updated 4 years ago
- PyTorch Implementation Of WS-DAN(See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Cl…☆174Oct 31, 2026Updated last week
- [PR 2022, Highly Cited Paper] Learning Attention-Guided Pyramidal Features for Few-shot Fine-grained Recognition☆16Jan 23, 2023Updated 3 years ago
- Official implementation of "Fine-Grained Object Classification via Self-Supervised Pose Alignment".☆24Mar 31, 2022Updated 4 years ago
- Reimplementation of Mutual-Channel Loss for Fine-Grained Image Classification.☆37May 26, 2020Updated 6 years ago
- Associating Multi-Scale Receptive Fields for Fine-grained Recognition (ICIP2020)☆29Oct 9, 2020Updated 6 years ago
- Proton VPN Special Offer - Get 70% off • AdSpecial partner offer. Trusted by over 100 million users worldwide. Tested, Approved and Recommended by Experts.
- Pytorch implementation of "ICCV2019-Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization"☆25Apr 9, 2020Updated 6 years ago
- Code release for Your “Flamingo” is My “Bird”: Fine-Grained, or Not (CVPR 2021 Oral)☆66Sep 27, 2023Updated 3 years ago
- ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021☆110Jun 30, 2022Updated 4 years ago
- This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MM…☆260Dec 29, 2020Updated 5 years ago
- This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-N…☆422Oct 4, 2022Updated 4 years ago
- ☆16Jul 6, 2020Updated 6 years ago
- Destruction and Construction Learning for Fine-grained Image Recognition☆592Jun 10, 2020Updated 6 years ago
- Code for the ICML 2021 paper "Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer"☆12Aug 17, 2021Updated 5 years ago
- HIVE: Evaluating the Human Interpretability of Visual Explanations (ECCV 2022)☆22Jan 19, 2023Updated 3 years ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- SCOPS: Self-Supervised Co-Part Segmentation (CVPR'19)☆219Dec 8, 2022Updated 3 years ago
- Dynamic Group Convolution for Accelerating Convolutional Neural Networks (ECCV 2020)☆127Jun 1, 2021Updated 5 years ago
- Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification (TIP2021)☆51Jul 25, 2024Updated 2 years ago
- The Curious Layperson: Fine-Grained Image Recognition without Expert Labels (BMVC 2021 best student paper)☆25Jun 2, 2022Updated 4 years ago
- Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition reimplement in Pytorch.☆105Jul 11, 2020Updated 6 years ago
- Code for the Paper Learning Hierarchy Aware Features for Reducing Mistake Severity, accepted in ECCV 2022☆15Dec 16, 2022Updated 3 years ago
- Gradients as Features for Deep Representation Learning☆43Mar 8, 2020Updated 6 years ago
- [IJCAI 2021 & IJCV 2022] Learning Visual Words for Weakly-Supervised Semantic Segmentation☆28Feb 11, 2022Updated 4 years ago
- [ACM MM 2018] Attribute-Aware Attention Model for Fine-grained Representation Learning☆158Aug 31, 2019Updated 7 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- [CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition☆61Dec 11, 2022Updated 3 years ago
- Code for CVPR2021 paper 'Where and What? Examining Interpretable Disentangled Representations'.☆45Feb 10, 2022Updated 4 years ago
- Unsupervised Part Discovery from Contrastive Reconstruction (NeurIPS 2021)☆67Dec 6, 2023Updated 2 years ago
- Code for GCPR 2020 Oral : "Unsupervised Part Discovery by Unsupervised Disentanglement"☆34Oct 2, 2020Updated 6 years ago
- FineGAN: Unsupervised Hierarchical Disentanglement for Fine-grained Object Generation and Discovery☆281Aug 2, 2021Updated 5 years ago
- SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)☆129Dec 26, 2020Updated 5 years ago
- Code for the ICLR 2022 paper "Attention-based interpretability with Concept Transformers"☆41Sep 17, 2025Updated last year