我们考虑一个由特征提取器组成的物体识别模型,Fθ:X→ZFθ:X→Z,其中ZZ是特征嵌入空间,以及分类器Gψ:Z→RCGψ:Z→RC,其中CC表示标签空间中的类别数。 3.5. Proxy-based Contrastive Learning Softmax 损失在学习类别代理方面效率高,实现了快速且安全的收敛,但不考虑样本与样本之间的关系。基于对比的损失利用了...
PCL: Proxy-based Contrastive Learning for Domain Generalization (CVPR'22) Official PyTorch implementation of PCL: Proxy-based Contrastive Learning in Domain Generalization. Xufeng Yao, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, Bei Yu Note that this project is built upo...
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning Huiwei Lin, Baoquan Zhang, Shanshan Feng*, Xutao Li, Yunming Ye Harbin Institute of Technology, Shenzhen {linhuiwei, zhangbaoquan}@stu.hit.edu.cn, {victor fengss, lixutao, y...
Metric learning attempts to minimize a loss function in order to transform data into a more optimal representation for further applications. In this paper, we compare 4 different types of loss functions, e.g. 2 pair-based losses (Contrastive loss and Triplet Margin Ranking loss), and 2 proxy...
UCCH. proposes a novel momentum optimizer for learnable hashing in contrastive learning and designs a cross-modal ranking learning loss. Evaluation Protocols. We evaluated our method by comparing it with baseline approaches on two cross-modal retrieval tasks: image-to-text retrieval (I→T) and tex...
UCCH. proposes a novel momentum optimizer for learnable hashing in contrastive learning and designs a cross-modal ranking learning loss. Evaluation Protocols. We evaluated our method by comparing it with baseline approaches on two cross-modal retrieval tasks: image-to-text retrieval (I→T) and tex...
UCCH. proposes a novel momentum optimizer for learnable hashing in contrastive learning and designs a cross-modal ranking learning loss. Evaluation Protocols. We evaluated our method by comparing it with baseline approaches on two cross-modal retrieval tasks: image-to-text retrieval (I→T) and tex...