Search Results for author: Xiaoguang Li

Found 28 papers, 7 papers with code

CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification

no code implementations ICCV 2023 Rabab Abdelfattah, Qing Guo, Xiaoguang Li, XiaoFeng Wang, Song Wang

Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels.

Classification Multi-Label Image Classification +2

SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting

no code implementations26 Jul 2023 Canyu Zhang, Qing Guo, Xiaoguang Li, Renjie Wan, Hongkai Yu, Ivor Tsang, Song Wang

Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.

Image Inpainting Image Reconstruction +2

CNN-based Dendrite Core Detection from Microscopic Images of Directionally Solidified Ni-base Alloys

no code implementations21 May 2023 Xiaoguang Li

To make HSD only focus on the feature of hard samples of dendrite cores, we destroy the structure of the easy samples of dendrites which are detected by ESD and force HSD to learn the feature of hard samples.

Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal

no code implementations18 May 2023 Xiaoguang Li, Qing Guo, Pingping Cai, Wei Feng, Ivor Tsang, Song Wang

State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs, which are desired to complete two distinct tasks via shared weights, i. e., data restoration for shadow regions and identical mapping for non-shadow regions.

Image Shadow Removal Shadow Removal

Leveraging Inpainting for Single-Image Shadow Removal

1 code implementation ICCV 2023 Xiaoguang Li, Qing Guo, Rabab Abdelfattah, Di Lin, Wei Feng, Ivor Tsang, Song Wang

In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.

Image Inpainting Image Shadow Removal +1

Retrieval-based Disentanglement with Distant Supervision

no code implementations15 Dec 2022 Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Lei Chen

Disentangled representation learning remains challenging as ground truth factors of variation do not naturally exist.

Cross-Modal Retrieval Disentanglement +2

Pre-training Language Models with Deterministic Factual Knowledge

no code implementations20 Oct 2022 Shaobo Li, Xiaoguang Li, Lifeng Shang, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

Further experiments on question-answering datasets show that trying to learn a deterministic relationship with the proposed methods can also help other knowledge-intensive tasks.

Knowledge Probing Question Answering

Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages

1 code implementation18 Oct 2022 Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, Jimmy Lin

MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval across 18 different languages, which collectively encompass over three billion native speakers around the world.

Information Retrieval Retrieval

How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis

no code implementations Findings (ACL) 2022 Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

1 code implementation ACL 2022 Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).

Open-Domain Question Answering Passage Retrieval +1

Unsupervised Open-Domain Question Answering

no code implementations31 Aug 2021 Pengfei Zhu, Xiaoguang Li, Jian Li, Hai Zhao

Open-domain Question Answering (ODQA) has achieved significant results in terms of supervised learning manner.

Machine Reading Comprehension Open-Domain Question Answering

JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting

1 code implementation9 Jul 2021 Qing Guo, Xiaoguang Li, Felix Juefei-Xu, Hongkai Yu, Yang Liu, Song Wang

In this paper, for the first time, we formulate image inpainting as a mix of two problems, predictive filtering and deep generation.

Image Inpainting

Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack

no code implementations1 Nov 2020 Haonan Yan, Xiaoguang Li, Hui Li, Jiamin Li, Wenhai Sun, Fenghua Li

In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model.

Model extraction

SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval

no code implementations2 Oct 2020 Yang Bai, Xiaoguang Li, Gang Wang, Chaoliang Zhang, Lifeng Shang, Jun Xu, Zhaowei Wang, Fangshan Wang, Qun Liu

Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.

Language Modelling Retrieval +1

Blur-Attention: A boosting mechanism for non-uniform blurred image restoration

no code implementations19 Aug 2020 Xiaoguang Li, Feifan Yang, Kin Man Lam, Li Zhuo, Jiafeng Li

Our method can adaptively select the weights of the extracted features according to the spatially varying blur features, and dynamically restore the images.

Deblurring Image Restoration +1

Dynamics of ferromagnetic bimerons driven by spin currents and magnetic fields

no code implementations25 May 2020 Laichuan Shen, Xiaoguang Li, Jing Xia, Lei Qiu, Xichao Zhang, Oleg A. Tretiakov, Motohiko Ezawa, Yan Zhou

Numerical simulations demonstrate that two bimerons with opposite signs of topological numbers can be created simultaneously in a ferromagnetic thin film via current-induced spin torques.

Mesoscale and Nanoscale Physics

Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism

no code implementations6 Feb 2020 Xiaoguang Li, Hui Li, Haonan Yan, Zelei Cheng, Wenhai Sun, Hui Zhu

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.

Model extraction regression

DUMA: Reading Comprehension with Transposition Thinking

3 code implementations26 Jan 2020 Pengfei Zhu, Hai Zhao, Xiaoguang Li

Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.

Language Modelling Machine Reading Comprehension +1

Dual Multi-head Co-attention for Multi-choice Reading Comprehension

no code implementations1 Jan 2020 Pengfei Zhu, Hai Zhao, Xiaoguang Li

Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.

Language Modelling Machine Reading Comprehension +1

A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems

no code implementations23 Sep 2019 Wei Cai, Xiaoguang Li, Lizuo Liu

In this paper, we propose a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

10 code implementations31 Aug 2019 Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu

The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.

named-entity-recognition Named Entity Recognition +5

PhaseDNN - A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning

no code implementations3 May 2019 Wei Cai, Xiaoguang Li, Lizuo Liu

Due to the phase shift, each DNN achieves the speed of convergence as in the low frequency range.

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