Search Results for author: Chen Wu

Found 71 papers, 28 papers with code

PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation

no code implementations EMNLP 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +8

SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images

no code implementations28 Aug 2023 HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang

These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages.

Change Detection

T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images

1 code implementation4 Aug 2023 Huan Zhong, Chen Wu

Siamese structure focuses on extracting object features at different times but lacks attention to change information, which leads to false alarms and missed detections.

Change Detection

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

1 code implementation23 Jul 2023 HaoNan Guo, Bo Du, Chen Wu, Chengxi Han, Liangpei Zhang

To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD.

Change Detection Metric Learning

Expediting Building Footprint Segmentation from High-resolution Remote Sensing Images via progressive lenient supervision

1 code implementation23 Jul 2023 HaoNan Guo, Bo Du, Chen Wu, Xin Su, Liangpei Zhang

The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness.

Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction

no code implementations23 Jul 2023 HaoNan Guo, Xin Su, Chen Wu, Bo Du, Liangpei Zhang

Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks.

Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detection

1 code implementation20 Jul 2023 Zhenghui Zhao, Lixiang Ru, Chen Wu

Specifically, change missing refer to the situation that the WSCD model fails to predict any changed pixels, even though the image-level label indicates changed, and vice versa for change fabricating.

Change Detection Weakly-supervised Learning

Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices

no code implementations5 May 2023 Yali Zheng, Chen Wu, Peizheng Cai, Zhiqiang Zhong, Hongda Huang, Yuqi Jiang

Therefore, this study provides an effective solution for resource-constraint IoT smart health devices in PPG artifact detection.

GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection

no code implementations18 Apr 2023 Meiqi Hu, Chen Wu, Liangpei Zhang

High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response.

Change Detection

EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection

no code implementations24 Mar 2023 Meiqi Hu, Chen Wu, Bo Du

Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration.

Change Detection Clustering

Learning to Backdoor Federated Learning

1 code implementation6 Mar 2023 Henger Li, Chen Wu, Sencun Zhu, Zizhan Zheng

In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training.

Backdoor Attack Federated Learning +1

HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection

no code implementations21 Feb 2023 Chengxi Han, Chen Wu, Bo Du

Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem.

Change Detection

Grafting Pre-trained Models for Multimodal Headline Generation

no code implementations14 Nov 2022 Lingfeng Qiao, Chen Wu, Ye Liu, Haoyuan Peng, Di Yin, Bo Ren

In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model.

Headline Generation Language Modelling

Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

1 code implementation3 Oct 2022 Hongruixuan Chen, Naoto Yokoya, Chen Wu, Bo Du

Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations and two difference images are generated based on the similarity levels.

Change Detection Graph Representation Learning

Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models

1 code implementation14 Sep 2022 Chen Wu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Yixing Fan, Maarten de Rijke, Xueqi Cheng

A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack.

Information Retrieval Retrieval

VLMAE: Vision-Language Masked Autoencoder

no code implementations19 Aug 2022 Sunan He, Taian Guo, Tao Dai, Ruizhi Qiao, Chen Wu, Xiujun Shu, Bo Ren

Image and language modeling is of crucial importance for vision-language pre-training (VLP), which aims to learn multi-modal representations from large-scale paired image-text data.

Language Modelling Question Answering +4

Exploiting Feature Diversity for Make-up Temporal Video Grounding

no code implementations12 Aug 2022 Xiujun Shu, Wei Wen, Taian Guo, Sunan He, Chen Wu, Ruizhi Qiao

This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022.

Video Grounding

HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection

no code implementations20 Jul 2022 Meiqi Hu, Chen Wu, Liangpei Zhang

Only the positive samples at the same location of bi-temporal HSIs are created and forced to be aligned, aiming at learning the spectral difference-invariant features.

Change Detection Self-Supervised Learning

DeepPERF: A Deep Learning-Based Approach For Improving Software Performance

no code implementations27 Jun 2022 Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu

Additionally, we evaluate DeepPERF on 50 open source C# repositories on GitHub using both benchmark and unit tests and find that our model is able to suggest valid performance improvements that can improve both CPU usage and Memory allocations.

Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection

no code implementations23 May 2022 Meiqi Hu, Chen Wu, Bo Du

Hyperspectral anomalous change detection has been a challenging task for its emphasis on the dynamics of small and rare objects against the prevalent changes.

Change Detection Contrastive Learning

A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints

no code implementations9 May 2022 Junwen Ding, Liangcai Song, Siyuan Li, Chen Wu, Ronghua He, Zhouxing Su, Zhipeng Lü

Computing workflows in heterogeneous multiprocessor systems are frequently modeled as directed acyclic graphs of tasks and data blocks, which represent computational modules and their dependencies in the form of data produced by a task and used by others.

Job Shop Scheduling Scheduling

Learning from Drivers to Tackle the Amazon Last Mile Routing Research Challenge

1 code implementation9 May 2022 Chen Wu, Yin Song, Verdi March, Eden Duthie

On the one hand, our method encodes driver know-how by learning a sequential probability model from historical routes at the zone level, where each zone contains a few parcel stops.

Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

1 code implementation25 Apr 2022 Haojie Huang, Gongming Zhou, Xuejun Liu, Lei Deng, Chen Wu, Dachuan Zhang, Hui Liu

We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological feature in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes.

Contrastive Learning whole slide images

PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models

no code implementations4 Apr 2022 Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng

We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list.

Document Ranking Information Retrieval +1

Programming Language Agnostic Mining of Code and Language Pairs with Sequence Labeling Based Question Answering

no code implementations21 Mar 2022 Changran Hu, Akshara Reddi Methukupalli, Yutong Zhou, Chen Wu, Yubo Chen

In particular, we propose to apply the BIO tagging scheme instead of the conventional binary scheme to mine the code solutions which are often composed of multiple blocks of a post.

Question Answering

Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling

1 code implementation10 Feb 2022 Lixiang Ru, Bo Du, Yibing Zhan, Chen Wu

In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.

Classification Weakly supervised Semantic Segmentation +1

Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus

1 code implementation27 Jan 2022 Chen Wu, Ming Yan

Different from typical information retrieval tasks, code search requires to bridge the semantic gap between the programming language and natural language, for better describing intrinsic concepts and semantics.

Code Search Information Retrieval +4

Dual-Tasks Siamese Transformer Framework for Building Damage Assessment

no code implementations26 Jan 2022 Hongruixuan Chen, Edoardo Nemni, Sofia Vallecorsa, Xi Li, Chen Wu, Lars Bromley

Considering the frontier advances of Transformer architecture in the computer vision field, in this paper, we present the first attempt at designing a Transformer-based damage assessment architecture (DamFormer).

Disaster Response Extracting Buildings In Remote Sensing Images +1

Federated Unlearning with Knowledge Distillation

no code implementations24 Jan 2022 Chen Wu, Sencun Zhu, Prasenjit Mitra

Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data.

Federated Learning Knowledge Distillation

Binary Change Guided Hyperspectral Multiclass Change Detection

no code implementations8 Dec 2021 Meiqi Hu, Chen Wu, Bo Du, Liangpei Zhang

In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches.

Change Detection

Exploring Forensic Dental Identification with Deep Learning

1 code implementation NeurIPS 2021 Yuan Liang, Weikun Han, Liang Qiu, Chen Wu, Yiting shao, Kun Wang, Lei He

In this work, we pioneer to study deep learning for dental forensic identification based on panoramic radiographs.

LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator

no code implementations4 Nov 2021 Zhuofu Tao, Chen Wu, Yuan Liang, Lei He

In this work, we propose LW-GCN, a lightweight FPGA-based accelerator with a software-hardware co-designed process to tackle irregularity in computation and memory access in GCN inference.


Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer

no code implementations18 Sep 2021 Hongruixuan Chen, Chen Wu, Yonghao Xu, Bo Du

To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain.

Ranked #32 on Synthetic-to-Real Translation on GTAV-to-Cityscapes Labels (using extra training data)

Self-Supervised Learning Semantic Segmentation +2

Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images

1 code implementation18 Aug 2021 Hongruixuan Chen, Chen Wu, Bo Du

With the goal of designing a quite deep architecture to obtain more precise CD results while simultaneously decreasing parameter numbers to improve efficiency, in this work, we present a very deep and efficient CD network, entitled EffCDNet.

Change Detection

Distilling Transformers for Neural Cross-Domain Search

no code implementations6 Aug 2021 Colin B. Clement, Chen Wu, Dawn Drain, Neel Sundaresan

Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks.

Code Search Data Augmentation +3

Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix

no code implementations17 Jul 2021 Wenshuo Wang, Chen Wu, Liang Cheng, Yang Zhang

The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program can be transformed to a translation task.

Program Repair Translation

AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations

1 code implementation Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation 2021 Jie Zhao, Bojie Li, Wang Nie, Zhen Geng, Renwei Zhang, Xiong Gao, Bin Cheng, Chen Wu, Yun Cheng, Zheng Li, Peng Di, Kun Zhang, Xuefeng Jin

Existing tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy.

Code Generation Management +1

Generating Bug-Fixes Using Pretrained Transformers

no code implementations16 Apr 2021 Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan

In this work we introduce DeepDebug: a data-driven program repair approach which learns to detect and fix bugs in Java methods mined from real-world GitHub repositories.

Denoising Program Repair

Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers

no code implementations12 Apr 2021 Dawn Drain, Changran Hu, Chen Wu, Mikhail Breslav, Neel Sundaresan

To demonstrate the effectiveness of our model designs, we perform extensive experiments with CodeSearchNet which contains template functions and CoNaLa which contains Stack Overflow intent-snippet pairs.

Code Search Retrieval

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

no code implementations2 Mar 2021 Chen Wu, Sihan Zhu, Jiaqi Yang, Meiqi Hu, Bo Du, Liangpei Zhang, Lefei Zhang, Chengxi Han, Meng Lan

Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.

Learning to Truncate Ranked Lists for Information Retrieval

no code implementations25 Feb 2021 Chen Wu, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng

One is the widely adopted metric such as F1 which acts as a balanced objective, and the other is the best F1 under some minimal recall constraint which represents a typical objective in professional search.

Information Retrieval Retrieval

Mitigating Backdoor Attacks in Federated Learning

no code implementations28 Oct 2020 Chen Wu, Xian Yang, Sencun Zhu, Prasenjit Mitra

To minimize the pruning influence on test accuracy, we can fine-tune after pruning, and the attack success rate drops to 6. 4%, with only a 1. 7% loss of test accuracy.

Federated Learning

Hyperspectral Anomaly Change Detection Based on Auto-encoder

1 code implementation27 Oct 2020 Meiqi Hu, Chen Wu, Liangpei Zhang, Bo Du

In the ACDA model, two systematic auto-encoder (AE) networks are deployed to construct two predictors from two directions.

Change Detection

An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images

no code implementations26 Jun 2020 Chen Wu, Yinong Guo, HaoNan Guo, Jingwen Yuan, Lixiang Ru, Hongruixuan Chen, Bo Du, Liangpei Zhang

The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.

Anomaly Detection Time Series +1

DSDANet: Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection

no code implementations16 Jun 2020 Hongruixuan Chen, Chen Wu, Bo Du, Liangpei Zhang

By optimizing the network parameters and kernel coefficients with the source labeled data and target unlabeled data, DSDANet can learn transferrable feature representation that can bridge the discrepancy between two domains.

Change Detection Domain Adaptation

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

1 code implementation3 Jun 2020 Lixiang Ru, Bo Du, Chen Wu

In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings.

Change Detection General Classification +2

The GLEAM 4-Jy (G4Jy) Sample: II. Host-galaxy identification for individual sources

1 code implementation27 Apr 2020 Sarah V. White, Thomas M. O. Franzen, Chris J. Riseley, O. Ivy Wong, Anna D. Kapińska, Natasha Hurley-Walker, Joseph R. Callingham, Kshitij Thorat, Chen Wu, Paul Hancock, Richard W. Hunstead, Nick Seymour, Jesse Swan, Randall Wayth, John Morgan, Rajan Chhetri, Carole Jackson, Stuart Weston, Martin Bell, B. M. Gaensler, Melanie Johnston-Hollitt, André Offringa, Lister Staveley-Smith

This is the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we have previously used a combination of visual inspection, cross-checks against the literature, and internal matching to identify the 'brightest' radio-sources ($S_{\mathrm{151MHz}} >$ 4 Jy) in the extragalactic catalogue (Galactic latitude, $|b| >$ 10 deg).

Astrophysics of Galaxies

The GLEAM 4-Jy (G4Jy) Sample: I. Definition and the catalogue

1 code implementation27 Apr 2020 Sarah V. White, Thomas M. O. Franzen, Chris J. Riseley, O. Ivy Wong, Anna D. Kapińska, Natasha Hurley-Walker, Joseph R. Callingham, Kshitij Thorat, Chen Wu, Paul Hancock, Richard W. Hunstead, Nick Seymour, Jesse Swan, Randall Wayth, John Morgan, Rajan Chhetri, Carole Jackson, Stuart Weston, Martin Bell, Bi-Qing For, B. M. Gaensler, Melanie Johnston-Hollitt, André Offringa, Lister Staveley-Smith

Of these G4Jy sources, 78 are resolved by the MWA (Phase-I) synthesised beam ($\sim$2 arcmin at 200 MHz), and we label 67% of the sample as 'single', 26% as 'double', 4% as 'triple', and 3% as having 'complex' morphology at $\sim$1 GHz (45-arcsec resolution).

Astrophysics of Galaxies

On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond

1 code implementation ACL 2020 Chen Wu, Prince Zizhuang Wang, William Yang Wang

To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching.

Dialogue Generation Language Modelling +1

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

2 code implementations14 Apr 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +8

Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images

no code implementations13 Apr 2020 Hongruixuan Chen, Chen Wu, Bo Du, Liangepei Zhang

In this paper, we propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection.

Change Detection Domain Adaptation

Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

2 code implementations18 Dec 2019 Chen Wu, Hongruixuan Chen, Bo Do, Liangpei Zhang

Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection.

Change Detection Clustering

Symmetric Regularization based BERT for Pair-wise Semantic Reasoning

1 code implementation8 Sep 2019 Weidi Xu, Xingyi Cheng, Kunlong Chen, Wei Wang, Bin Bi, Ming Yan, Chen Wu, Luo Si, Wei Chu, Taifeng Wang

To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).

Machine Reading Comprehension Natural Language Inference +1

Incorporating External Knowledge into Machine Reading for Generative Question Answering

no code implementations IJCNLP 2019 Bin Bi, Chen Wu, Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li

Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context.

Answer Generation Generative Question Answering +1

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding

no code implementations ICLR 2020 Wei Wang, Bin Bi, Ming Yan, Chen Wu, Zuyi Bao, Jiangnan Xia, Liwei Peng, Luo Si

Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.

Language Modelling Linguistic Acceptability +6

Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning

no code implementations13 Aug 2019 Jiangnan Xia, Chen Wu, Ming Yan

This paper focuses on how to take advantage of external relational knowledge to improve machine reading comprehension (MRC) with multi-task learning.

Language Modelling Machine Reading Comprehension +1

Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks

3 code implementations27 Jun 2019 Hongruixuan Chen, Chen Wu, Bo Du, Liangpei Zhang

Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively.

Change Detection

A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer

1 code implementation ACL 2019 Chen Wu, Xuancheng Ren, Fuli Luo, Xu sun

Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision.

Style Transfer Text Style Transfer +1

ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data

7 code implementations1 Apr 2019 Foivos I. Diakogiannis, François Waldner, Peter Caccetta, Chen Wu

Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications.

Scene Understanding Semantic Segmentation

Optimizing seed inputs in fuzzing with machine learning

no code implementations7 Feb 2019 Liang Cheng, Yang Zhang, Yi Zhang, Chen Wu, Zhangtan Li, Yu Fu, Haisheng Li

Our experiments on a set of widely used PDF viewers demonstrate that the improved seed inputs produced by our framework could significantly increase the code coverage of the target program and the likelihood of detecting program crashes.

Cryptography and Security

Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

no code implementations3 Dec 2018 Bo Du, Lixiang Ru, Chen Wu, Liangpei Zhang

In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection.

Change Detection

A Deep Cascade Model for Multi-Document Reading Comprehension

no code implementations28 Nov 2018 Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen

To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.

Machine Reading Comprehension Question Answering +2

The MWA GLEAM 4-Jy (G4Jy) Sample

no code implementations2 Oct 2018 Sarah V. White, Thomas M. O. Franzen, O. Ivy Wong, Anna D. Kapinska, Chris Riseley, Paul Hancock, Joseph Callingham, Richard Hunstead, Natasha Hurley-Walker, Chen Wu, Nick Seymour, Jesse Swan, Randall Wayth, John S. Morgan, Rajan Chhetri, Carole Jackson, Stuart Weston, Tom Mauch

These were observed at low radio-frequencies as part of the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, which is a continuum survey conducted using the Murchison Widefield Array (MWA).

Astrophysics of Galaxies

Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach

no code implementations6 Mar 2018 Xi Fang, Zengmao Wang, Xinyao Tang, Chen Wu

Simultaneously, our proposed method makes full use of the label information, and the proposed active learning is designed based on multiple classes.

Active Learning Informativeness

DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge

2 code implementations24 Feb 2017 Chen Wu, Rodrigo Tobar, Kevin Vinsen, Andreas Wicenec, Dave Pallot, Baoqiang Lao, Ruonan Wang, Tao An, Mark Boulton, Ian Cooper, Richard Dodson, Markus Dolensky, Ying Mei, Feng Wang

The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for processing large astronomical datasets at a scale required by the Square Kilometre Array Phase 1 (SKA1).

Distributed, Parallel, and Cluster Computing Instrumentation and Detectors

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