342 citations · 1.1k across the 21 of their papers we have counts for
9 papers · 1 filter
Semantic Cross Attention for Few-shot Learning
Bin Xiao, Chien-Liang Liu, Wen-Hoar Hsaio
Few-shot learning (FSL) has attracted considerable attention recently. Among existing approaches, the metric-based method aims to train an embedding network that can make similar s…
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training
Haoxuan You, Luowei Zhou, Bin Xiao +5
Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a…
TinyViT: Fast Pretraining Distillation for Small Vision Transformers
Kan Wu, Jinnian Zhang, Houwen Peng +4
Vision transformer (ViT) recently has drawn great attention in computer vision due to its remarkable model capability. However, most prevailing ViT models suffer from huge number o…
i-Code: An Integrative and Composable Multimodal Learning Framework
Ziyi Yang, Yuwei Fang, Chenguang Zhu +17
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to…
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks
Zhecan Wang, Noel Codella, Yen-Chun Chen +8
Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples,…
MiniViT: Compressing Vision Transformers with Weight Multiplexing
Jinnian Zhang, Houwen Peng, Kan Wu +4
Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT models suffer from huge number of parameters,…