This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).
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Updated
Sep 22, 2021 - Python
This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).
Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)
Implementing RepNet(a two-stream multitask learning network) to do vehicle Re-identification, vehicle search(or vehicle match) with PyTorch 可用于车辆细粒度识别,车辆再识别,车辆匹配,车辆检索,RepNet/MDNet的一种PyTorch实现
[ICLR'24] Democratizing Fine-grained Visual Recognition with Large Language Models
Code release for Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV2020)
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)
TF-Implementation of "Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition"
CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission
[CVPR 2022 Challenge Rank 1st] The official code for V2L: Leveraging Vision and Vision-language Models into Large-scale Product Retrieval.
[WACV'23] Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-grained Environments
Bilinear CNNs in PyTorch
Pytorch implementation of CVPR'16 paper "Learning Deep Representations of Fine-Grained Visual Descriptions", by Reed et al.
Code release for "Making a Bird AI Expert Work for You and Me (TPAMI 2023)".
Code release for Your “An Erudite Fine-Grained Visual Classification Model (CVPR 2023)"
unofficial PyTorch implementation of Look into object paper (CVPR2020).
Classifying images into coarse and fine classes. https://web.cse.iitk.ac.in/users/cs783/asm2/
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