Search Results for author: Meng Cao

Found 25 papers, 9 papers with code

A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization

no code implementations20 Apr 2022 Meng Cao

Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text.

Abstractive Text Summarization

Jacobian Norm for Unsupervised Source-Free Domain Adaptation

no code implementations7 Apr 2022 Weikai Li, Meng Cao, Songcan Chen

Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain.

Domain Adaptation

PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation

no code implementations28 Mar 2022 Shancong Mou, Meng Cao, Haoping Bai, Ping Huang, Jianjun Shi, Jiulong Shan

To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.

Anomaly Detection

Unsupervised Pre-training for Temporal Action Localization Tasks

1 code implementation25 Mar 2022 Can Zhang, Tianyu Yang, Junwu Weng, Meng Cao, Jue Wang, Yuexian Zou

These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization.

Contrastive Learning Representation Learning +4

Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey

no code implementations3 Mar 2022 Shancong Mou, Meng Cao, Zhendong Hong, Ping Huang, Jiulong Shan, Jianjun Shi

Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process.

Synthetic Data Generation

Information Gain Propagation: a new way to Graph Active Learning with Soft Labels

1 code implementation ICLR 2022 Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.

Active Learning

Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation

no code implementations22 Nov 2021 Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan

On active learning task, our method achieves 97. 0% Top-1 Accuracy on CIFAR10 with 0. 1% annotated data, and 83. 9% Top-1 Accuracy on CIFAR100 with 10% annotated data.

Active Learning Representation Learning

RIM: Reliable Influence-based Active Learning on Graphs

1 code implementation NeurIPS 2021 Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.

Active Learning

On Pursuit of Designing Multi-modal Transformer for Video Grounding

no code implementations EMNLP 2021 Meng Cao, Long Chen, Mike Zheng Shou, Can Zhang, Yuexian Zou

Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression.

Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization

1 code implementation ACL 2022 Meng Cao, Yue Dong, Jackie Chi Kit Cheung

State-of-the-art abstractive summarization systems often generate \emph{hallucinations}; i. e., content that is not directly inferable from the source text.

Abstractive Text Summarization reinforcement-learning

UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing

no code implementations12 Aug 2021 Meng Cao, HaoZhi Huang, Hao Wang, Xuan Wang, Li Shen, Sheng Wang, Linchao Bao, Zhifeng Li, Jiebo Luo

Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.

3D Reconstruction Face Reenactment +3

All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection

no code implementations24 Jun 2021 Meng Cao, Can Zhang, Dongming Yang, Yuexian Zou

Compared to the traditional single-stage segmentation network, our NASK conducts the detection in a coarse-to-fine manner with the first stage segmentation spotting the rectangle text proposals and the second one retrieving compact representations.

Instance Segmentation Semantic Segmentation

BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer

no code implementations NeurIPS 2021 Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan

While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space.

Neural Architecture Search Quantization

Video Frame Interpolation via Structure-Motion based Iterative Fusion

no code implementations11 May 2021 Xi Li, Meng Cao, Yingying Tang, Scott Johnston, Zhendong Hong, Huimin Ma, Jiulong Shan

Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation.

Frame Optical Flow Estimation +1

RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection

no code implementations30 Apr 2021 Dongming Yang, Yuexian Zou, Can Zhang, Meng Cao, Jie Chen

Upon the frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions.

Frame Human-Object Interaction Detection

CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning

1 code implementation CVPR 2021 Can Zhang, Meng Cao, Dongming Yang, Jie Chen, Yuexian Zou

In this paper, we argue that learning by comparing helps identify these hard snippets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short.

Contrastive Learning Frame +3

Quantum error-correcting codes from matrix-product codes related to quasi-orthogonal matrices and quasi-unitary matrices

no code implementations31 Dec 2020 Meng Cao

quasi-unitary matrices) as the defining matrices of matrix-product codes over $\mathbb{F}_{q}$ (resp.

Information Theory Information Theory Quantum Physics 11T55, 11T71, 81P45, 94B05

Factual Error Correction for Abstractive Summarization Models

1 code implementation EMNLP 2020 Meng Cao, Yue Dong, Jiapeng Wu, Jackie Chi Kit Cheung

Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.

Abstractive Text Summarization

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

1 code implementation EMNLP 2020 Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, William L. Hamilton

Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

Imputation Knowledge Graph Completion +1

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

1 code implementation4 Aug 2020 Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

COVID-19 Diagnosis Decision Making +1

Task-agnostic Temporally Consistent Facial Video Editing

no code implementations3 Jul 2020 Meng Cao, Hao-Zhi Huang, Hao Wang, Xuan Wang, Li Shen, Sheng Wang, Linchao Bao, Zhifeng Li, Jiebo Luo

Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.

3D Reconstruction Frame

All you need is a second look: Towards Tighter Arbitrary shape text detection

no code implementations26 Apr 2020 Meng Cao, Yuexian Zou

Specifically, \textit{NASK} consists of a Text Instance Segmentation network namely \textit{TIS} (\(1^{st}\) stage), a Text RoI Pooling module and a Fiducial pOint eXpression module termed as \textit{FOX} (\(2^{nd}\) stage).

Instance Segmentation Scene Text Detection +1

Unsupervised Domain Adaptation Through Transferring both the Source-Knowledge and Target-Relatedness Simultaneously

no code implementations18 Mar 2020 Qing Tian, Yanan Zhu, Chuang Ma, Meng Cao

Unsupervised domain adaptation (UDA) is an emerging research topic in the field of machine learning and pattern recognition, which aims to help the learning of unlabeled target domain by transferring knowledge from the source domain.

Unsupervised Domain Adaptation

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