57 citations · 242 across the 21 of their papers we have counts for
5 papers · 1 filter
Deep Incubation: Training Large Models by Divide-and-Conquering
Zanlin Ni, Yulin Wang, Jiangwei Yu +3
Recent years have witnessed a remarkable success of large deep learning models. However, training these models is challenging due to high computational costs, painfully slow conver…
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones
Yulin Wang, Yang Yue, Rui Lu +4
The superior performance of modern deep networks usually comes with a costly training procedure. This paper presents a new curriculum learning approach for the efficient training o…
AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition
Yulin Wang, Yang Yue, Xinhong Xu +6
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e.g., allocating the majority of c…
Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation
Wenxuan Ma, Jinming Zhang, Shuang Li +3
Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. M…
Glance and Focus Networks for Dynamic Visual Recognition
Gao Huang, Yulin Wang, Kangchen Lv +4
Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the rema…