Search Results for author: Hong Zhou

Found 17 papers, 5 papers with code

BiViT: Extremely Compressed Binary Vision Transformer

no code implementations14 Nov 2022 Yefei He, Zhenyu Lou, Luoming Zhang, Weijia Wu, Bohan Zhuang, Hong Zhou

To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.


Real-time End-to-End Video Text Spotter with Contrastive Representation Learning

no code implementations18 Jul 2022 Wejia Wu, Zhuang Li, Jiahong Li, Chunhua Shen, Hong Zhou, Size Li, Zhongyuan Wang, Ping Luo

Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.

Contrastive Learning Representation Learning +1

Binarizing by Classification: Is soft function really necessary?

no code implementations16 May 2022 Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou

Binary neural network leverages the $Sign$ function to binarize real values, and its non-derivative property inevitably brings huge gradient errors during backpropagation.

 Ranked #1 on Binarization on ImageNet (Top 1 Accuracy metric)

Binarization Classification +2

Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization

no code implementations8 Apr 2022 Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou

In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.

Data Free Quantization

End-to-End Video Text Spotting with Transformer

1 code implementation20 Mar 2022 Weijia Wu, Yuanqiang Cai, Chunhua Shen, Debing Zhang, Ying Fu, Hong Zhou, Ping Luo

Recent video text spotting methods usually require the three-staged pipeline, i. e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results.

Text Spotting

The Observability in Unobservable Systems

no code implementations11 Jan 2022 Wei Kang, Liang Xu, Hong Zhou

In this paper, we introduce the concept of observability of targeted state variables for systems that may not be fully observable.

Contrastive Learning of Semantic and Visual Representations for Text Tracking

no code implementations30 Dec 2021 Zhuang Li, Weijia Wu, Mike Zheng Shou, Jiahong Li, Size Li, Zhongyuan Wang, Hong Zhou

Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.

Contrastive Learning

Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

no code implementations ICCV 2021 Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, ShiLiang Pu, Hong Zhou

Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.

Unsupervised Anomaly Detection

Polygon-free: Unconstrained Scene Text Detection with Box Annotations

1 code implementation26 Nov 2020 Weijia Wu, Enze Xie, Ruimao Zhang, Wenhai Wang, Hong Zhou, Ping Luo

For example, without using polygon annotations, PSENet achieves an 80. 5% F-score on TotalText [3] (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.

Scene Text Detection

A Local Search Framework for Experimental Design

no code implementations29 Oct 2020 Lap Chi Lau, Hong Zhou

We present a local search framework to design and analyze both combinatorial algorithms and rounding algorithms for experimental design problems.

Experimental Design Fairness

TextCohesion: Detecting Text for Arbitrary Shapes

no code implementations22 Apr 2019 Weijia Wu, Jici Xing, Hong Zhou

In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions.

Curved Text Detection

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

1 code implementation25 Dec 2017 Jiachi Zhang, Xiaolei Shen, Tianqi Zhuo, Hong Zhou

Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization.

Brain Tumor Segmentation General Classification +4

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network

no code implementations13 Nov 2017 Xiaolei Shen, Jiachi Zhang, Chenjun Yan, Hong Zhou

The core of our method is to extract features of images based on convolutional neural network and achieve classification by classifier.

Classification General Classification

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