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20212023
most citedTinyViT: Fast Pretraining Distillation for Small Vision Transformers

22 citations · 54 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV20224 cited

Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling

Rui Wang, Zuxuan Wu, Dongdong Chen +6

Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of mo…

cs.CV20223 cited

Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis

Wan-Cyuan Fan, Yen-Chun Chen, Dongdong Chen +3

Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both im…

cs.CV20224 cited

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…

cs.CV202222 cited

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…

cs.CV20223 cited

Bootstrapped Masked Autoencoders for Vision BERT Pretraining

Xiaoyi Dong, Jianmin Bao, Ting Zhang +6

We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1)…

cs.CV2022

Should All Proposals be Treated Equally in Object Detection?

Yunsheng Li, Yinpeng Chen, Xiyang Dai +7

The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. Previous works have emphasized detectors implemented with effi…