Search Results for author: Baigui Sun

Found 14 papers, 7 papers with code

Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN

1 code implementation27 May 2022 Siyuan Li, Di wu, Fang Wu, Zelin Zang, Kai Wang, Lei Shang, Baigui Sun, Hao Li, Stan. Z. Li

We observe that MIM essentially teaches the model to learn better middle-level interactions among patches and extract more generalized features.

Image Classification Self-Supervised Learning

TransZero: Attribute-guided Transformer for Zero-Shot Learning

1 code implementation3 Dec 2021 Shiming Chen, Ziming Hong, Yang Liu, Guo-Sen Xie, Baigui Sun, Hao Li, Qinmu Peng, Ke Lu, Xinge You

Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.

Zero-Shot Learning

HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

1 code implementation NeurIPS 2021 Shiming Chen, Guo-Sen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao

Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e., structure adaptation and distribution adaptation.

Transfer Learning Zero-Shot Learning

Unsupervised Domain Adaptation By Optimal Transportation Of Clusters Between Domains

no code implementations29 Sep 2021 Yang Liu, Zhipeng Zhou, Lei Shang, Baigui Sun, Hao Li, Rong Jin

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain.

Transfer Learning Unsupervised Domain Adaptation

Dash: Semi-Supervised Learning with Dynamic Thresholding

no code implementations1 Sep 2021 Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin

In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models.

Semi-Supervised Image Classification

Digging into Uncertainty in Self-supervised Multi-view Stereo

1 code implementation ICCV 2021 Hongbin Xu, Zhipeng Zhou, Yali Wang, Wenxiong Kang, Baigui Sun, Hao Li, Yu Qiao

Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background.

Image Reconstruction Self-Supervised Learning

Align Yourself: Self-supervised Pre-training for Fine-grained Recognition via Saliency Alignment

no code implementations30 Jun 2021 Di wu, Siyuan Li, Zelin Zang, Kai Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li

In this paper, we first point out that current contrastive methods are prone to memorizing background/foreground texture and therefore have a limitation in localizing the foreground object.

Contrastive Learning Image Classification +2

An Efficient Training Approach for Very Large Scale Face Recognition

1 code implementation CVPR 2022 Kai Wang, Shuo Wang, Panpan Zhang, Zhipeng Zhou, Zheng Zhu, Xiaobo Wang, Xiaojiang Peng, Baigui Sun, Hao Li, Yang You

This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.

 Ranked #1 on Face Verification on IJB-C (training dataset metric)

Face Recognition Face Verification

Learning to Cluster Faces via Transformer

no code implementations23 Apr 2021 Jinxing Ye, Xioajiang Peng, Baigui Sun, Kai Wang, Xiuyu Sun, Hao Li, Hanqing Wu

In this paper, we repurpose the well-known Transformer and introduce a Face Transformer for supervised face clustering.

Face Clustering

MogFace: Towards a Deeper Appreciation on Face Detection

2 code implementations CVPR 2022 Yang Liu, Fei Wang, Jiankang Deng, Zhipeng Zhou, Baigui Sun, Hao Li

As a result, practical solutions on label assignment, scale-level data augmentation, and reducing false alarms are necessary for advancing face detectors.

Data Augmentation Face Detection

AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition

no code implementations18 Dec 2020 Kai Wang, Yuxin Gu, Xiaojiang Peng, Panpan Zhang, Baigui Sun, Hao Li

The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one dataset to another one.

Facial Expression Recognition

Robust Optimization over Multiple Domains

no code implementations19 May 2018 Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, Hao Li

Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency.

Fine-Grained Visual Categorization

Deep CTR Prediction in Display Advertising

no code implementations20 Sep 2016 Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, Xian-Sheng Hua

Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model.

Click-Through Rate Prediction

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