51 citations · 258 across the 16 of their papers we have counts for
6 papers · 2 filters
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity
Jiangmeng Li, Wenwen Qiang, Changwen Zheng +4
While self-supervised learning techniques are often used to mining implicit knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective repres…
Interventional Contrastive Learning with Meta Semantic Regularizer
Wenwen Qiang, Jiangmeng Li, Changwen Zheng +2
Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner. Although the prevailing CL model has achieved great progress, in…
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
Gang Li, Heliang Zheng, Daqing Liu +3
Recently, significant progress has been made in masked image modeling to catch up to masked language modeling. However, unlike words in NLP, the lack of semantic decomposition of i…
Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt
Jiangmeng Li, Wenyi Mo, Wenwen Qiang +4
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. To boost the transferability of the pre-trained models,…
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation
Wenwen Qiang, Jiangmeng Li, Changwen Zheng +2
Unsupervised domain adaptation (UDA) requires source domain samples with clean ground truth labels during training. Accurately labeling a large number of source domain samples is t…
MetAug: Contrastive Learning via Meta Feature Augmentation
Jiangmeng Li, Wenwen Qiang, Changwen Zheng +2
What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include mor…