Intelligent algorithm for selecting suitable training classes for zero-shot object recognition that capture domain diversity and rarity
-
Updated
Jun 20, 2024 - Python
Intelligent algorithm for selecting suitable training classes for zero-shot object recognition that capture domain diversity and rarity
Course project for programming in AI 22fall (Peking university)
Keras Custom Layers of AdaCos and ArcFace contains experiments in caltech birds 2011(CUB-200-2011).
Image Classification Training Framework for Network Distillation
Official implementation of POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples (NeurIPS 2021)
An implementation of Contrastive Loss in PyTorch using Siamese Networks
A Tensorflow retrieval (space embedding) baseline. Metric learning baseline on CUB and Stanford Online Products.
A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.
👗3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective👗 Single-View 3D Reconstruction
This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)" (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu).
This is a pytorch re-implementation of Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition
Add a description, image, and links to the cub-200-2011 topic page so that developers can more easily learn about it.
To associate your repository with the cub-200-2011 topic, visit your repo's landing page and select "manage topics."