activity
20162024
most citedSelf-supervised Feature-Gate Coupling for Dynamic Network Pruning

2 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.QM2024

Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation

Xiangqian Zhu, Mengnan Shi, Xuexin Yu +7

Atrial fibrillation is a commonly encountered clinical arrhythmia associated with stroke and increased mortality. Since professional medical knowledge is required for annotation, e…

cs.CV2022★ 1 cited

Proposal Distribution Calibration for Few-Shot Object Detection

Bohao Li, Chang Liu, Mengnan Shi +3

Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step…

cs.CV2022★ 1 cited

Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers

Yunjie Tian, Lingxi Xie, Jiemin Fang +6

The past year has witnessed a rapid development of masked image modeling (MIM). MIM is mostly built upon the vision transformers, which suggests that self-supervised visual represe…

cs.CV2021★ 2 cited

Self-supervised Feature-Gate Coupling for Dynamic Network Pruning

Mengnan Shi, Chang Liu, Jianbin Jiao +1

Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of feature…

cs.CV2016★ 1 cited

A scalable convolutional neural network for task-specified scenarios via knowledge distillation

Mengnan Shi, Fei Qin, Qixiang Ye +2

In this paper, we explore the redundancy in convolutional neural network, which scales with the complexity of vision tasks. Considering that many front-end visual systems are inter…