Search Results for author: Yin Tang

Found 5 papers, 1 papers with code

DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation

no code implementations6 Sep 2023 Guang Yang, Yin Tang, Zhijian Wu, Jun Li, Jianhua Xu, Xili Wan

Recent mainstream masked distillation methods function by reconstructing selectively masked areas of a student network from the feature map of its teacher counterpart.

Knowledge Distillation object-detection +1

Holistic Prototype Attention Network for Few-Shot VOS

1 code implementation16 Jul 2023 Yin Tang, Tao Chen, Xiruo Jiang, Yazhou Yao, Guo-Sen Xie, Heng-Tao Shen

Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames.

Graph Attention Semantic Segmentation +2

AMD: Adaptive Masked Distillation for Object Detection

no code implementations31 Jan 2023 Guang Yang, Yin Tang, Jun Li, Jianhua Xu, Xili Wan

As a general model compression paradigm, feature-based knowledge distillation allows the student model to learn expressive features from the teacher counterpart.

Knowledge Distillation Model Compression +3

Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices

no code implementations5 Jun 2020 Xin Cheng, Lei Zhang, Yin Tang, Yue Liu, Hao Wu, Jun He

For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations.

Human Activity Recognition

Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors

no code implementations8 May 2020 Yin Tang, Qi Teng, Lei Zhang, Fuhong Min, Jun He

A set of lower-dimensional filters is used as Lego bricks to be stacked for conventional filters, which does not rely on any special network structure.

Human Activity Recognition Time Series Analysis

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