Search Results for author: Manyuan Zhang

Found 12 papers, 5 papers with code

Towards Large-scale Masked Face Recognition

no code implementations25 Oct 2023 Manyuan Zhang, Bingqi Ma, Guanglu Song, Yunxiao Wang, Hongsheng Li, Yu Liu

During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms.

Face Recognition

Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection

no code implementations ICCV 2023 Manyuan Zhang, Guanglu Song, Yu Liu, Hongsheng Li

We observe that different regions of interest in the visual feature map are suitable for performing query classification and box localization tasks, even for the same object.

Classification object-detection +1

VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation

1 code implementation ICCV 2023 Xiaoyu Shi, Zhaoyang Huang, Weikang Bian, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li

We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner.

Optical Flow Estimation

Towards Robust Face Recognition with Comprehensive Search

no code implementations29 Aug 2022 Manyuan Zhang, Guanglu Song, Yu Liu, Hongsheng Li

To eliminate the bias of single-aspect research and provide an overall understanding of the face recognition model design, we first carefully design the search space for each aspect, then a comprehensive search method is introduced to jointly search optimal data cleaning, architecture, and loss function design.

Face Recognition Robust Face Recognition

Switchable K-Class Hyperplanes for Noise-Robust Representation Learning

no code implementations ICCV 2021 Boxiao Liu, Guanglu Song, Manyuan Zhang, Haihang You, Yu Liu

When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane.

Model Optimization Representation Learning +1

Discriminability Distillation in Group Representation Learning

no code implementations ECCV 2020 Manyuan Zhang, Guanglu Song, Hang Zhou, Yu Liu

We show the discrimiability knowledge has good properties that can be distilled by a light-weight distillation network and can be generalized on the unseen target set.

Representation Learning

Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020

no code implementations20 Jul 2020 Haisheng Su, Jinyuan Feng, Hao Shao, Zhenyu Jiang, Manyuan Zhang, Wei Wu, Yu Liu, Hongsheng Li, Junjie Yan

Specifically, in order to generate high-quality proposals, we consider several factors including the video feature encoder, the proposal generator, the proposal-proposal relations, the scale imbalance, and ensemble strategy.

Temporal Action Localization

1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020

2 code implementations16 Jun 2020 Siyu Chen, Junting Pan, Guanglu Song, Manyuan Zhang, Hao Shao, Ziyi Lin, Jing Shao, Hongsheng Li, Yu Liu

This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.

Relation Network Spatio-Temporal Action Localization +1

Top-1 Solution of Multi-Moments in Time Challenge 2019

1 code implementation12 Mar 2020 Manyuan Zhang, Hao Shao, Guanglu Song, Yu Liu, Junjie Yan

In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019.

Action Recognition Video Understanding

Towards Flops-constrained Face Recognition

1 code implementation2 Sep 2019 Yu Liu, Guanglu Song, Manyuan Zhang, Jihao Liu, Yucong Zhou, Junjie Yan

Large scale face recognition is challenging especially when the computational budget is limited.

Lightweight Face Recognition

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