1 citations · 1 across the 6 of their papers we have counts for
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From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling
Jinhong Lin, Cheng-En Wu, Huanran Li +3
Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning paradigm for visual representation learning, enabling models to acquire rich visual representations b…
Accelerating Augmentation Invariance Pretraining
Jinhong Lin, Cheng-En Wu, Yibing Wei +1
Our work tackles the computational challenges of contrastive learning methods, particularly for the pretraining of Vision Transformers (ViTs). Despite the effectiveness of contrast…
Patch Ranking: Efficient CLIP by Learning to Rank Local Patches
Cheng-En Wu, Jinhong Lin, Yu Hen Hu +1
Contrastive image-text pre-trained models such as CLIP have shown remarkable adaptability to downstream tasks. However, they face challenges due to the high computational requireme…
Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning
Yibing Wei, Abhinav Gupta, Pedro Morgado
Masked Image Modeling (MIM) has emerged as a promising method for deriving visual representations from unlabeled image data by predicting missing pixels from masked portions of ima…