5 papers · 1 filter
Scaling Up Audio-Synchronized Visual Animation: An Efficient Training Paradigm
Lin Zhang, Zefan Cai, Yufan Zhou +10
Recent advances in audio-synchronized visual animation enable control of video content using audios from specific classes. However, existing methods rely heavily on expensive manua…
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…
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…
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…
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…