Huayi Zhou (周华毅)

I'm a PhD student at Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), advised by Prof. Hongtao Lu from year 2020. Previously, I was a master student at SJTU advised by Prof. Ruimin Shen from year 2017 to 2020, and an undergraduate student at Hunan University (HNU) majoring in Computer Science and Technology from year 2013 to 2017.

My research interests lie in computer vision (e.g., object detection and pose estimation), also combining with some machine learning techniques such as multi-task learning, domain adaptation, domain generalization and semi-supervised learning. I am committed to pursuing simple yet efficient design, and exploring data/label efficient learning. Most of my works are about inferring the physical world (location, association, pose, shape, etc) from RGB images. Representative papers are highlighted.

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News

● [2024.05] I have passed the doctoral dissertation defense on May 29, 2024.
● [2024.04] Our SemiUHPE for Semi-Supervised Unconstrained Head Pose Estimation in the Wild is released.
● [2024.02] Our MultiAugs for boosting Semi-Supervised 2D Human Pose Estimation is released.
● [2024.01] PBADet: A One-Stage Anchor-Free Approach for Part-Body Association is accepted by ICLR 2024.
● [2024.01] BPJDet: Extended Object Representation for Generic Body-Part Joint Detection is accepted by TPAMI 2024.
● [2023.07] I've received the ICME 2023 Best Student Paper Runner Up Award in 14 July 2023, Brisbane, Australia.
● [2023.04] CONFETI for Domain Adaptive Semantic Segmentation has been accepted by CVPR Workshop 2023.
● [2023.04] BPJDet for Human Body-Part Joint Detection and Association has been accepted by ICME 2023.
● [2023.03] StuArt for Individualized Classroom Observation of Students has been accepted by ICASSP 2023. (Note: StuArt is one of the key part of the project AIClass: Automatic Teaching Assistance System Towards Classrooms for K-12 Education.)
● [2023.03] SSDA-YOLO for YOLOv5-based Domain Adaptive Object Detection has been accepted by CVIU 2023.
● [2023.02] Our work DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles is released.

Activities

● Conference Reviewer: ACCV'2024, ECCV'2024, ICME'2024, CVPR'2024, ICCV'2023, CVPR'2023, ECCV'2022
● Journal Reviewer: Transactions on Image Processing (TIP), Transactions on Circuits and Systems for Video Technology (TCSVT), Transactions on Intelligent Vehicles (TIV)

Research Summary

Publications

We propose the first semi-supervised unconstrained head pose estimation (SemiUHPE) method, which can leverage a large amount of unlabeled wild head images. SemiUHPE is robust to estimate wild challenging heads (e.g., heavy blur, extreme illumination, severe occlusion, atypical pose, and invisible face).

Our method MultiAugs contains two vital components: (1) New advanced collaborative augmentation combinations; (2) Multi-path predictions of strongly augment inputs with diverse augmentations. Either of them can help to boost the performance of Semi-Supervised 2D Human Pose Estimation.

This paper presents PBADet, a novel one-stage, anchor-free approach for part-body association detection. Building upon the anchor-free object representation across multi-scale feature maps, it introduces a singular part-to-body center offset that effectively encapsulates the relationship between parts and their parent bodies.

The journal version of BPJDet. It has various new functions (Multiple Body-Parts Joint Detection and two downstream applications including Body-Head for Accurate Crowd Counting and Body-Hand for Hand Contact Estimation)

To overcome the domain gap between synthetic and real-world datasets for semantic segmentation, this paper present CONtrastive FEaTure and pIxel alignment (CONFETI) for bridging the domain gap at both the pixel and feature levels.

A novel extended object representation that integrates the center location offsets of body or its parts, and construct a dense one-stage Body-Part Joint Detector (BPJDet). This design is simple yet efficient.

StuArt is a novel automatic system designed for the individualized classroom observation. We proposed some pedagogical approaches in signal processing for K-12 education. (Note: StuArt is one of the key part of the project AIClass.)

This paper focuses on the full-range Multi-Person Head Pose Estimation (MPHPE) problem. We firstly construct two benchmarks by extracting GT labels for head detection and head orientation from public datasets AGORA and CMU Panoptic. Then, we propose a direct end-to-end simple baseline named DirectMHP based on YOLOv5.

This paper presents a novel semi-supervised domain adaptive YOLO (SSDA-YOLO) based method to improve cross-domain detection performance by integrating the compact one-stage stronger detector YOLOv5 with domain adaptation.

An automatic hand-raiser recognition algorithm to show who raise their hands in real classroom scenarios.

Experience

The template of this website is borrowed from Jonathan T. Barron.