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...
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...
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...
Our experiments show that the Proxy-Anchor loss could achieve 70.8% accuracy on average compared to the Proxy-NCA loss, Triplet Margin Ranking loss and Contrastive loss which could only achieve 65.5%, 62.2%, and 36.6% respectively. Furthermore, we also present the qualitative results using high...
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...