Search Results for author: Shuai Shao

Found 27 papers, 6 papers with code

EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE

no code implementations23 Aug 2023 Junyi Chen, Longteng Guo, Jia Sun, Shuai Shao, Zehuan Yuan, Liang Lin, Dongyu Zhang

Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed.

Image-text matching Question Answering +5

ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition

1 code implementation22 May 2023 Shuai Shao, Yu Guan, Bing Zhai, Paolo Missier, Thomas Ploetz

Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer -- we develop three "boosters" -- R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively.

Data Augmentation Human Activity Recognition

FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation

no code implementations26 Apr 2023 Yan Wang, Jian Cheng, Yixin Chen, Shuai Shao, Lanyun Zhu, Zhenzhou Wu, Tao Liu, Haogang Zhu

In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model.

Image Segmentation Medical Image Segmentation +3

Algorithm Selection for Deep Active Learning with Imbalanced Datasets

no code implementations14 Feb 2023 Jifan Zhang, Shuai Shao, Saurabh Verma, Robert Nowak

Extensive experiments in multi-class and multi-label applications demonstrate TAILOR's effectiveness in achieving accuracy comparable or better than that of the best of the candidate algorithms.

Active Learning

Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

no code implementations23 Dec 2022 Shuai Shao, Yu Guan, Xin Guan, Paolo Missier, Thomas Ploetz

What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non periodic, and occur less frequently when compared with the often large amount of irrelevant background activities.

Human Activity Recognition Time Series Analysis

MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning

no code implementations9 Oct 2022 Zijia Zhao, Longteng Guo, Xingjian He, Shuai Shao, Zehuan Yuan, Jing Liu

Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover.

Question Answering Representation Learning +5

Efficient population coding of sensory stimuli

no code implementations24 Jul 2022 Shuai Shao, Markus Meister, Julijana Gjorgjieva

Here we derive a general theory of optimal population coding with neuronal activation functions of any shape, different types of noise and heterogeneous firing rates of the neurons by maximizing the Shannon mutual information between a stimulus and the neuronal spiking output subject to a constrain on the maximal firing rate.

Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation

no code implementations5 Apr 2022 Bo Yuan, Danpei Zhao, Shuai Shao, Zehuan Yuan, Changhu Wang

In two typical cross-domain semantic segmentation tasks, i. e., GTA5 to Cityscapes and SYNTHIA to Cityscapes, our method achieves the state-of-the-art segmentation accuracy.

Road Segmentation Unsupervised Domain Adaptation

SSDL: Self-Supervised Dictionary Learning

no code implementations3 Dec 2021 Shuai Shao, Lei Xing, Wei Yu, Rui Xu, Yanjiang Wang, BaoDi Liu

Inspired by the concept of self-supervised learning (e. g., setting the pretext task to generate a universal model for the downstream task), we propose a Self-Supervised Dictionary Learning (SSDL) framework to address this challenge.

Dictionary Learning Human Activity Recognition +1

MDFM: Multi-Decision Fusing Model for Few-Shot Learning

no code implementations1 Dec 2021 Shuai Shao, Lei Xing, Rui Xu, Weifeng Liu, Yan-Jiang Wang, Bao-Di Liu

Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model.

Few-Shot Learning

CIM: Class-Irrelevant Mapping for Few-Shot Classification

no code implementations7 Sep 2021 Shuai Shao, Lei Xing, Yixin Chen, Yan-Jiang Wang, Bao-Di Liu, Yicong Zhou

(2) Use the FEM to extract the features of novel data (with few labeled samples and totally different categories from base data), then classify them with the to-be-designed classifier.

Classification Dictionary Learning +1

SAHDL: Sparse Attention Hypergraph Regularized Dictionary Learning

no code implementations23 Oct 2020 Shuai Shao, Rui Xu, Yan-Jiang Wang, Weifeng Liu, Bao-Di Liu

In this paper, we propose a hypergraph based sparse attention mechanism to tackle this issue and embed it into dictionary learning.

Dictionary Learning

DLDL: Dynamic Label Dictionary Learning via Hypergraph Regularization

no code implementations23 Oct 2020 Shuai Shao, Mengke Wang, Rui Xu, Yan-Jiang Wang, Bao-Di Liu

To tackle this issue, we propose a Dynamic Label Dictionary Learning (DLDL) algorithm to generate the soft label matrix for unlabeled data.

Dictionary Learning

A Dichotomy for Real Boolean Holant Problems

no code implementations16 May 2020 Shuai Shao, Jin-Yi Cai

We prove a complexity dichotomy for Holant problems on the boolean domain with arbitrary sets of real-valued constraint functions.

Computational Complexity

Shape Robust Text Detection with Progressive Scale Expansion Network

16 code implementations CVPR 2019 Wenhai Wang, Enze Xie, Xiang Li, Wenbo Hou, Tong Lu, Gang Yu, Shuai Shao

Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.

Optical Character Recognition (OCR) Scene Text Detection +1

Label Embedded Dictionary Learning for Image Classification

1 code implementation7 Mar 2019 Shuai Shao, Yan-Jiang Wang, Bao-Di Liu, Weifeng Liu, Rui Xu

Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification.

Classification Dictionary Learning +2

Scene Text Detection with Supervised Pyramid Context Network

2 code implementations21 Nov 2018 Enze Xie, Yuhang Zang, Shuai Shao, Gang Yu, Cong Yao, Guangyao Li

We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.

Instance Segmentation Scene Text Detection +2

CrowdHuman: A Benchmark for Detecting Human in a Crowd

1 code implementation30 Apr 2018 Shuai Shao, Zijian Zhao, Boxun Li, Tete Xiao, Gang Yu, Xiangyu Zhang, Jian Sun

There are a total of $470K$ human instances from the train and validation subsets, and $~22. 6$ persons per image, with various kinds of occlusions in the dataset.

Ranked #7 on Pedestrian Detection on Caltech (using extra training data)

Human Detection Object Detection +1

Repulsion Loss: Detecting Pedestrians in a Crowd

2 code implementations CVPR 2018 Xinlong Wang, Tete Xiao, Yuning Jiang, Shuai Shao, Jian Sun, Chunhua Shen

In this paper, we first explore how a state-of-the-art pedestrian detector is harmed by crowd occlusion via experimentation, providing insights into the crowd occlusion problem.

Ranked #9 on Pedestrian Detection on Caltech (using extra training data)

Pedestrian Detection regression

Object Detection via Aspect Ratio and Context Aware Region-based Convolutional Networks

no code implementations2 Dec 2016 Bo Li, Tianfu Wu, Shuai Shao, Lun Zhang, Rufeng Chu

This paper presents a method of integrating a mixture of object models and region-based convolutional networks for accurate object detection.

object-detection Object Detection

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