42 citations · 99 across the 9 of their papers we have counts for
14 papers · 1 filter
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Yuting Gao, Jinfeng Liu, Zihan Xu +4
Large-scale vision-language pre-training has achieved promising results on downstream tasks. Existing methods highly rely on the assumption that the image-text pairs crawled from t…
Training-free Transformer Architecture Search
Qinqin Zhou, Kekai Sheng, Xiawu Zheng +5
Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks. The progresses are highly relevant to the architecture design, then it is worth…
RMNet: Equivalently Removing Residual Connection from Networks
Fanxu Meng, Hao Cheng, Jiaxin Zhuang +2
Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers…
ISTR: End-to-End Instance Segmentation with Transformers
Jie Hu, Liujuan Cao, Yao Lu +6
End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replac…
On Evolving Attention Towards Domain Adaptation
Kekai Sheng, Ke Li, Xiawu Zheng +5
Towards better unsupervised domain adaptation (UDA). Recently, researchers propose various domain-conditioned attention modules and make promising progresses. However, considering…
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning
Huixiang Luo, Hao Cheng, Fanxu Meng +4
Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods. These methods exhibit great p…