Search Results for author: Zitong Huang

Found 8 papers, 5 papers with code

LayerMatch: Do Pseudo-labels Benefit All Layers?

no code implementations20 Jun 2024 Chaoqi Liang, Guanglei Yang, Lifeng Qiao, Zitong Huang, Hongliang Yan, Yunchao Wei, WangMeng Zuo

Our approach, LayerMatch, which integrates these two strategies, can avoid the severe interference of noisy pseudo-labels in the linear classification layer while accelerating the clustering capability of the feature extraction layer.

Avg Clustering +1

IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks

no code implementations25 Apr 2024 Zitong Huang, Ze Chen, Bowen Dong, Chaoqi Liang, Erjin Zhou, WangMeng Zuo

Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models.

Image Classification object-detection +2

Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning

1 code implementation3 Jan 2024 Zitong Huang, Ze Chen, Zhixing Chen, Erjin Zhou, Xinxing Xu, Rick Siow Mong Goh, Yong liu, WangMeng Zuo, ChunMei Feng

When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.

Few-Shot Class-Incremental Learning Incremental Learning +1

ImaginaryNet: Learning Object Detectors without Real Images and Annotations

1 code implementation13 Oct 2022 Minheng Ni, Zitong Huang, Kailai Feng, WangMeng Zuo

Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model deployed to generate a photo-realistic image.

Image Generation Language Modelling +3

W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection

1 code implementation25 Jul 2022 Zitong Huang, Yiping Bao, Bowen Dong, Erjin Zhou, WangMeng Zuo

Generally, with given pseudo ground-truths generated from the well-trained WSOD network, we propose a two-module iterative training algorithm to refine pseudo labels and supervise better object detector progressively.

Object object-detection +2

Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs

no code implementations15 Oct 2021 Ryan Jacobs, Mingren Shen, YuHan Liu, Wei Hao, Xiaoshan Li, Ruoyu He, Jacob RC Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G. Field, Dane Morgan

In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model.

object-detection Object Detection +1

Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters

1 code implementation ICCV 2021 Bowen Dong, Zitong Huang, Yuelin Guo, Qilong Wang, Zhenxing Niu, WangMeng Zuo

In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset.

Object object-detection +3

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