Search Results for author: Baopu Li

Found 19 papers, 7 papers with code

Exploring Gradient Flow Based Saliency for DNN Model Compression

1 code implementation24 Oct 2021 Xinyu Liu, Baopu Li, Zhen Chen, Yixuan Yuan

Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead.

Image Classification Image Denoising +1

BN-NAS: Neural Architecture Search with Batch Normalization

1 code implementation ICCV 2021 BoYu Chen, Peixia Li, Baopu Li, Chen Lin, Chuming Li, Ming Sun, Junjie Yan, Wanli Ouyang

We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS).

Neural Architecture Search

PSViT: Better Vision Transformer via Token Pooling and Attention Sharing

no code implementations7 Aug 2021 BoYu Chen, Peixia Li, Baopu Li, Chuming Li, Lei Bai, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang

Then, a compact set of the possible combinations for different token pooling and attention sharing mechanisms are constructed.

Action Segmentation with Mixed Temporal Domain Adaptation

no code implementations15 Apr 2021 Min-Hung Chen, Baopu Li, Yingze Bao, Ghassan AlRegib

The main progress for action segmentation comes from densely-annotated data for fully-supervised learning.

Action Segmentation Domain Adaptation

No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODE

1 code implementation3 Apr 2021 HaoChih Lin, Baopu Li, Xin Zhou, Jiankun Wang, Max Q. -H. Meng

Interactions with either environments or expert policies during training are needed for most of the current imitation learning (IL) algorithms.

Imitation Learning

Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution

no code implementations CVPR 2021 Baoli Sun, Xinchen Ye, Baopu Li, Haojie Li, Zhihui Wang, Rui Xu

First, we design a cross-task distillation scheme that encourages DSR and DE networks to learn from each other in a teacher-student role-exchanging fashion.

Depth Estimation Super-Resolution +1

MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

1 code implementation CVPR 2021 Xiaoqing Guo, Chen Yang, Baopu Li, Yixuan Yuan

Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation.

Meta-Learning Semantic Segmentation +2

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

no code implementations25 Jan 2021 Wei Wang, Baopu Li, Shuhui Yang, Jing Sun, Zhengming Ding, Junyang Chen, Xiao Dong, Zhihui Wang, Haojie Li

From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one.

Domain Adaptation

AutoPruning for Deep Neural Network with Dynamic Channel Masking

no code implementations22 Oct 2020 Baopu Li, Yanwen Fan, Zhihong Pan, Gang Zhang

In the process of pruning, we utilize a searchable hyperparameter, remaining ratio, to denote the number of channels in each convolution layer, and then a dynamic masking process is proposed to describe the corresponding channel evolution.

AutoML Network Pruning

SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure

no code implementations22 Sep 2020 Weitao Feng, Zhihao Hu, Baopu Li, Weihao Gan, Wei Wu, Wanli Ouyang

Besides, we propose a new MOT evaluation measure, Still Another IDF score (SAIDF), aiming to focus more on identity issues. This new measure may overcome some problems of the previous measures and provide a better insight for identity issues in MOT.

Multi-Object Tracking

Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS

no code implementations2 Sep 2020 Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding

With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections.

Image Super-Resolution Neural Architecture Search

Cross-modality Person re-identification with Shared-Specific Feature Transfer

no code implementations CVPR 2020 Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, Nenghai Yu

In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the re-identification performance.

Cross-Modality Person Re-identification Person Re-Identification

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