Search Results for author: Zhongqin Wu

Found 23 papers, 12 papers with code

Triplet Knowledge Distillation

no code implementations25 May 2023 Xijun Wang, Dongyang Liu, Meina Kan, Chunrui Han, Zhongqin Wu, Shiguang Shan

Distillation then begins in an online manner, and the teacher is only allowed to express solutions within the aforementioned subspace.

Face Recognition Image Classification +1

A Character-level Span-based Model for Mandarin Prosodic Structure Prediction

1 code implementation31 Mar 2022 Xueyuan Chen, Changhe Song, Yixuan Zhou, Zhiyong Wu, Changbin Chen, Zhongqin Wu, Helen Meng

In this paper, we propose a span-based Mandarin prosodic structure prediction model to obtain an optimal prosodic structure tree, which can be converted to corresponding prosodic label sequence.

Syntax-Aware Network for Handwritten Mathematical Expression Recognition

2 code implementations CVPR 2022 Ye Yuan, Xiao Liu, Wondimu Dikubab, Hui Liu, Zhilong Ji, Zhongqin Wu, Xiang Bai

In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network.

All-in-One Image Restoration for Unknown Corruption

1 code implementation CVPR 2022 Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, Xi Peng

In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels.

Image Restoration

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation

1 code implementation ICCV 2021 Yuxiang Wei, Yupeng Shi, Xiao Liu, Zhilong Ji, Yuan Gao, Zhongqin Wu, WangMeng Zuo

It simply encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation.

Disentanglement Image Generation

Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval

no code implementations5 Aug 2021 Xuri Ge, Fuhai Chen, Joemon M. Jose, Zhilong Ji, Zhongqin Wu, Xiao Liu

In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e. g., "dog $\to$ play $\to$ ball" in semantic structure for an image, and (ii) seeking explicit inter-modal structural and semantic correspondence between the visual and textual modalities.

Retrieval Semantic correspondence

UniCon: Unified Context Network for Robust Active Speaker Detection

no code implementations5 Aug 2021 Yuanhang Zhang, Susan Liang, Shuang Yang, Xiao Liu, Zhongqin Wu, Shiguang Shan, Xilin Chen

Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties.

Audio-Visual Active Speaker Detection

Locality-aware Channel-wise Dropout for Occluded Face Recognition

no code implementations20 Jul 2021 Mingjie He, Jie Zhang, Shiguang Shan, Xiao Liu, Zhongqin Wu, Xilin Chen

Furthermore, by randomly dropping out several feature channels, our method can well simulate the occlusion of larger area.

Face Recognition

Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension

1 code implementation15 Jul 2021 Shiting Xu, Guowei Xu, Peilei Jia, Wenbiao Ding, Zhongqin Wu, Zitao Liu

A TR writing question may include multiple requirements and a high-quality essay must respond to each requirement thoroughly and accurately.

Machine Reading Comprehension

A Multimodal Machine Learning Framework for Teacher Vocal Delivery Evaluation

1 code implementation15 Jul 2021 Hang Li, Yu Kang, Yang Hao, Wenbiao Ding, Zhongqin Wu, Zitao Liu

The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities.

BIG-bench Machine Learning

Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining

1 code implementation15 Jul 2021 Guowei Xu, Wenbiao Ding, Weiping Fu, Zhongqin Wu, Zitao Liu

Despite that pre-trained models achieve state-of-the-art performance in many NLP benchmarks, we prove that they are not robust to noisy texts generated by real OCR engines.

Optical Character Recognition Optical Character Recognition (OCR) +2

Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models

1 code implementation15 Jul 2021 Qiongqiong Liu, Tianqiao Liu, Jiafu Zhao, Qiang Fang, Wenbiao Ding, Zhongqin Wu, Feng Xia, Jiliang Tang, Zitao Liu

Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.

Sentence Completion

Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models

1 code implementation15 Jul 2021 Yang Hao, Hang Li, Wenbiao Ding, Zhongqin Wu, Jiliang Tang, Rose Luckin, Zitao Liu

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits.

Multi-Task Learning

FAIEr: Fidelity and Adequacy Ensured Image Caption Evaluation

no code implementations CVPR 2021 Sijin Wang, Ziwei Yao, Ruiping Wang, Zhongqin Wu, Xilin Chen

Then for evaluating the adequacy of the candidate caption, it highlights the image gist on the visual scene graph under the guidance of the reference captions.

Image Captioning

Towards the Memorization Effect of Neural Networks in Adversarial Training

no code implementations9 Jun 2021 Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang

In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.

Adversarial Robustness Memorization +1

Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser

1 code implementation18 Mar 2021 Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, WangMeng Zuo

To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images.

Denoising Test

AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text

no code implementations18 Dec 2020 Zhihong Shao, Zitao Liu, Jiyong Zhang, Zhongqin Wu, Minlie Huang

In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods.

Text Matching

Personalized Multimodal Feedback Generation in Education

no code implementations COLING 2020 Haochen Liu, Zitao Liu, Zhongqin Wu, Jiliang Tang

The automatic evaluation for school assignments is an important application of AI in the education field.

Text Generation

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