Search Results for author: Guoqiang Han

Found 15 papers, 8 papers with code

BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications

1 code implementation7 Sep 2023 Jiatai Lin, Guoqiang Han, Xuemiao Xu, Changhong Liang, Tien-Tsin Wong, C. L. Philip Chen, Zaiyi Liu, Chu Han

Class activation mapping~(CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation~(WSSS) and object localization~(WSOL).

Object Localization Weakly supervised Semantic Segmentation +1

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

1 code implementation24 Feb 2023 Tianpeng Deng, Yanqi Huang, Guoqiang Han, Zhenwei Shi, Jiatai Lin, Qi Dou, Zaiyi Liu, Xiao-jing Guo, C. L. Philip Chen, Chu Han

In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication.

Federated Learning

Unifying Global-Local Representations in Salient Object Detection with Transformer

1 code implementation5 Aug 2021 Sucheng Ren, Qiang Wen, Nanxuan Zhao, Guoqiang Han, Shengfeng He

In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers.

Decoder object-detection +2

Reciprocal Transformations for Unsupervised Video Object Segmentation

1 code implementation CVPR 2021 Sucheng Ren, Wenxi Liu, Yongtuo Liu, Haoxin Chen, Guoqiang Han, Shengfeng He

Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers.

Object Optical Flow Estimation +3

Spatially-Invariant Style-Codes Controlled Makeup Transfer

1 code implementation CVPR 2021 Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, Shengfeng He

In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process.

Decoder

Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading

no code implementations CVPR 2021 Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He

To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher.

Lip Reading Sentence +2

TENet: Triple Excitation Network for Video Salient Object Detection

no code implementations ECCV 2020 Sucheng Ren, Chu Han, Xin Yang, Guoqiang Han, Shengfeng He

In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations.

Object object-detection +2

Learning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyses

no code implementations1 Jul 2020 Jiazhou Chen, Guoqiang Han, Hongmin Cai, Defu Yang, Paul J. Laurienti, Martin Styner, Guorong Wu, Alzheimer's Disease Neuroimaging Initiative ADNI

To that end, we propose a novel connectome harmonic analysis framework to provide enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders.

Context-aware and Scale-insensitive Temporal Repetition Counting

1 code implementation CVPR 2020 Huaidong Zhang, Xuemiao Xu, Guoqiang Han, Shengfeng He

It avoids the heavy computation of exhaustively searching all the cycle lengths in the video, and, instead, it propagates the coarse prediction for further refinement in a hierarchical manner.

regression

Delving Into Salient Object Subitizing and Detection

no code implementations ICCV 2017 Shengfeng He, Jianbo Jiao, Xiaodan Zhang, Guoqiang Han, Rynson W. H. Lau

Experiments show that the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps.

Object object-detection +2

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