Search Results for author: Chang Liu

Found 236 papers, 78 papers with code

面向人工智能伦理计算的中文道德词典构建方法研究(Construction of a Chinese Moral Dictionary for Artificial Intelligence Ethical Computing)

no code implementations CCL 2020 Hongrui Wang, Chang Liu, Dong Yu

道德词典资源的建设是人工智能伦理计算的一个研究重点。由于道德行为复杂多样, 现有的英文道德词典分类体系并不完善, 而中文方面目前尚未有相关的词典资源, 理论体系和构建方法仍待探究。针对以上问题, 该文提出了面向人工智能伦理计算的中文道德词典构建任务, 设计了四类标签和四种类型, 得到包含25, 012个词的中文道德词典资源。实验结果表明, 该词典资源不仅能够使机器学会道德知识, 判断词的道德标签和类型, 而且能够为句子级别的道德文本分析提供数据支持。

ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation

no code implementations ACL 2022 Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan

To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.

Dialogue Generation Response Generation

Variance Reduction and Quasi-Newton for Particle-Based Variational Inference

no code implementations ICML 2020 Michael Zhu, Chang Liu, Jun Zhu

Particle-based Variational Inference methods (ParVIs), like Stein Variational Gradient Descent, are nonparametric variational inference methods that optimize a set of particles to best approximate a target distribution.

Bayesian Inference Riemannian optimization +1

Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations

no code implementations COLING 2022 Jiazhan Feng, Chongyang Tao, Zhen Li, Chang Liu, Tao Shen, Dongyan Zhao

In this paper, we propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.


基于跨语言双语预训练及Bi-LSTM的汉-越平行句对抽取方法(Chinese-Vietnamese Parallel Sentence Pair Extraction Method Based on Cross-lingual Bilingual Pre-training and Bi-LSTM)

no code implementations CCL 2020 Chang Liu, Shengxiang Gao, Zhengtao Yu, Yuxin Huang, Congcong You

汉越平行句对抽取是缓解汉越平行语料库数据稀缺的重要方法。平行句对抽取可转换为同一语义空间下的句子相似性分类任务, 其核心在于双语语义空间对齐。传统语义空间对齐方法依赖于大规模的双语平行语料, 越南语作为低资源语言获取大规模平行语料相对困难。针对这个问题本文提出一种利用种子词典进行跨语言双语预训练及Bi-LSTM(Bi-directional Long Short-Term Memory)的汉-越平行句对抽取方法。预训练中仅需要大量的汉越单语和一个汉越种子词典, 通过利用汉越种子词典将汉越双语映射到公共语义空间进行词对齐。再利用Bi-LSTM和CNN(Convolutional Neural Networks)分别提取句子的全局特征和局部特征从而最大化表示汉-越句对之间的语义相关性。实验结果表明, 本文模型在F1得分上提升7. 1%, 优于基线模型。

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

no code implementations28 Mar 2023 Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu

The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems.

Context-Aware Transformer for 3D Point Cloud Automatic Annotation

no code implementations27 Mar 2023 Xiaoyan Qian, Chang Liu, Xiaojuan Qi, Siew-Chong Tan, Edmund Lam, Ngai Wong

3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious.

Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

1 code implementation27 Mar 2023 Chang Liu, Weiming Zhang, Xiangru Lin, Wei zhang, Xiao Tan, Junyu Han, Xiaomao Li, Errui Ding, Jingdong Wang

It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.

Dense Object Detection object-detection +2

Frame Flexible Network

1 code implementation26 Mar 2023 Yitian Zhang, Yue Bai, Chang Liu, Huan Wang, Sheng Li, Yun Fu

To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly.

Video Recognition

Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning

1 code implementation25 Mar 2023 Peng Jin, Jinfa Huang, Pengfei Xiong, Shangxuan Tian, Chang Liu, Xiangyang Ji, Li Yuan, Jie Chen

Contrastive learning-based video-language representation learning approaches, e. g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs.

Contrastive Learning Question Answering +4

Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation

no code implementations23 Mar 2023 Kehan Li, Yian Zhao, Zhennan Wang, Zesen Cheng, Peng Jin, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen

Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis.

Interactive Segmentation

DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

no code implementations17 Mar 2023 Peng Jin, Hao Li, Zesen Cheng, Kehan Li, Xiangyang Ji, Chang Liu, Li Yuan, Jie Chen

Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i. e., p(candidates|query).

Retrieval Video Retrieval

Parallel Vertex Diffusion for Unified Visual Grounding

no code implementations13 Mar 2023 Zesen Cheng, Kehan Li, Peng Jin, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen

An intuitive materialization of our paradigm is Parallel Vertex Diffusion (PVD) to directly set vertex coordinates as the generation target and use a diffusion model to train and infer.

Visual Grounding

Stock Price Prediction Using Temporal Graph Model with Value Chain Data

no code implementations7 Mar 2023 Chang Liu, Sandra Paterlini

Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks.

Stock Price Prediction

Image as Set of Points

1 code implementation2 Mar 2023 Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu

Context clusters (CoCs) view an image as a set of unorganized points and extract features via simplified clustering algorithm.

Ultra-low Precision Multiplication-free Training for Deep Neural Networks

no code implementations28 Feb 2023 Chang Liu, Rui Zhang, Xishan Zhang, Yifan Hao, Zidong Du, Xing Hu, Ling Li, Qi Guo

The energy-efficient works try to decrease the precision of multiplication or replace the multiplication with energy-efficient operations such as addition or bitwise shift, to reduce the energy consumption of FP32 multiplications.


Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain

no code implementations28 Feb 2023 Chang Liu, Wenzhao Xiang, Yuan He, Hui Xue, Shibao Zheng, Hang Su

To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement.

Data Augmentation

A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking

no code implementations28 Feb 2023 Chang Liu, Yinpeng Dong, Wenzhao Xiang, Xiao Yang, Hang Su, Jun Zhu, Yuefeng Chen, Yuan He, Hui Xue, Shibao Zheng

In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.

Adversarial Robustness Benchmarking +2

Particle-based Online Bayesian Sampling

1 code implementation28 Feb 2023 Yifan Yang, Chang Liu, Zheng Zhang

Online optimization has gained increasing interest due to its capability of tracking real-world streaming data.

Variational Inference

MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

1 code implementation3 Feb 2023 Henghui Ding, Chang Liu, Shuting He, Xudong Jiang, Philip H. S. Torr, Song Bai

However, since the target objects in these existing datasets are usually relatively salient, dominant, and isolated, VOS under complex scenes has rarely been studied.

Semantic Segmentation Video Object Segmentation +1

UATVR: Uncertainty-Adaptive Text-Video Retrieval

no code implementations16 Jan 2023 Bo Fang, Wenhao Wu, Chang Liu, Yu Zhou, Min Yang, Yuxin Song, Fu Li, Weiping Wang, Xiangyang Ji, Wanli Ouyang

In the refined embedding space, we represent text-video pairs as probabilistic distributions where prototypes are sampled for matching evaluation.

Retrieval Semantic correspondence +1

Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling

1 code implementation31 Dec 2022 Xin Ma, Chang Liu, Chunyu Xie, Long Ye, Yafeng Deng, Xiangyang Ji

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency.

object-detection Object Detection +2

Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach

no code implementations28 Dec 2022 Chang Liu, Shuangyang Li, Weijie Yuan, Xuemeng Liu, Derrick Wing Kwan Ng

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC).

Adam: Dense Retrieval Distillation with Adaptive Dark Examples

no code implementations20 Dec 2022 Chang Liu, Chongyang Tao, Xiubo Geng, Tao Shen, Dongyan Zhao, Can Xu, Binxing Jiao, Daxin Jiang

Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space.

Knowledge Distillation Retrieval

DQnet: Cross-Model Detail Querying for Camouflaged Object Detection

no code implementations16 Dec 2022 Wei Sun, Chengao Liu, Linyan Zhang, Yu Li, Pengxu Wei, Chang Liu, Jialing Zou, Jianbin Jiao, Qixiang Ye

Optimizing a convolutional neural network (CNN) for camouflaged object detection (COD) tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue which inevitably leads to missing or redundant regions of objects.

object-detection Object Detection +1

Proposal Distribution Calibration for Few-Shot Object Detection

1 code implementation15 Dec 2022 Bohao Li, Chang Liu, Mengnan Shi, Xiaozhong Chen, Xiangyang Ji, Qixiang Ye

Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging.

Few-Shot Object Detection object-detection

THMA: Tencent HD Map AI System for Creating HD Map Annotations

no code implementations14 Dec 2022 Kun Tang, Xu Cao, Zhipeng Cao, Tong Zhou, Erlong Li, Ao Liu, Shengtao Zou, Chang Liu, Shuqi Mei, Elena Sizikova, Chao Zheng

THMA has been deployed by the Tencent Map team to provide services to downstream companies and users, serving over 1, 000 labeling workers and producing more than 30, 000 kilometers of HD map data per day at most.

Active Learning Weakly-supervised Learning

Robust Contracts with Exploration

no code implementations30 Nov 2022 Chang Liu

We study a two-period moral hazard problem; there are two agents, with identical action sets that are unknown to the principal.

ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation

no code implementations25 Nov 2022 Qiran Zou, Yu Yang, Wing Yin Cheung, Chang Liu, Xiangyang Ji

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise.

Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach

no code implementations23 Nov 2022 Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Robert Schober

With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements.

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

no code implementations22 Nov 2022 Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen

Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted.

Optical Character Recognition (OCR) Weakly supervised Semantic Segmentation +1

EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test

no code implementations19 Nov 2022 Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu

While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level representation learning and permutation-equivariant in node- and edge-level representation learning, their expressive power is commonly limited by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test.

Graph Representation Learning

Local Magnification for Data and Feature Augmentation

no code implementations15 Nov 2022 Kun He, Chang Liu, Stephen Lin, John E. Hopcroft

And further combination with our feature augmentation techniques, termed LOMA_IF&FO, can continue to strengthen the model and outperform advanced intensity transformation methods for data augmentation.

Data Augmentation Image Classification +2

Distilling Representations from GAN Generator via Squeeze and Span

1 code implementation6 Nov 2022 Yu Yang, Xiaotian Cheng, Chang Liu, Hakan Bilen, Xiangyang Ji

In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains.

Representation Learning

Local Manifold Augmentation for Multiview Semantic Consistency

no code implementations5 Nov 2022 Yu Yang, Wing Yin Cheung, Chang Liu, Xiangyang Ji

Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation.

Representation Learning Self-Supervised Learning

Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning

no code implementations2 Nov 2022 Yifei Zhang, Chang Liu, Yu Zhou, Weiping Wang, Qixiang Ye, Xiangyang Ji

In this paper, we present relation-aware contrastive self-supervised learning (ReCo) to integrate instance relations, i. e., global distribution relation and local interpolation relation, into the CSL framework in a plug-and-play fashion.

Self-Supervised Learning

Position-Aware Subgraph Neural Networks with Data-Efficient Learning

1 code implementation1 Nov 2022 Chang Liu, Yuwen Yang, Zhe Xie, Hongtao Lu, Yue Ding

2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure.

Contrastive Learning Representation Learning

VLT: Vision-Language Transformer and Query Generation for Referring Segmentation

1 code implementation28 Oct 2022 Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang

We propose a Vision-Language Transformer (VLT) framework for referring segmentation to facilitate deep interactions among multi-modal information and enhance the holistic understanding to vision-language features.

Ranked #2 on Referring Video Object Segmentation on Refer-YouTube-VOS (using extra training data)

Referring Expression Segmentation Referring Video Object Segmentation

Completely Heterogeneous Federated Learning

no code implementations28 Oct 2022 Chang Liu, Yuwen Yang, Xun Cai, Yue Ding, Hongtao Lu

Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i. i. d.

Federated Learning Knowledge Distillation

Matching entropy based disparity estimation from light field

no code implementations28 Oct 2022 Ligen Shi, Chang Liu, Di He, Xing Zhao, Jun Qiu

A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions.

Depth Estimation Disparity Estimation

Fuzzy Positive Learning for Semi-supervised Semantic Segmentation

no code implementations16 Oct 2022 Pengchong Qiao, Zhidan Wei, Yu Wang, Zhennan Wang, Guoli Song, Fan Xu, Xiangyang Ji, Chang Liu, Jie Chen

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.

Semi-Supervised Semantic Segmentation

NoMorelization: Building Normalizer-Free Models from a Sample's Perspective

no code implementations13 Oct 2022 Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu

The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and low generality.

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

no code implementations12 Oct 2022 Kehan Li, Zhennan Wang, Zesen Cheng, Runyi Yu, Yian Zhao, Guoli Song, Chang Liu, Li Yuan, Jie Chen

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e. g., unsupervised semantic segmentation (USS).

Image Segmentation Unsupervised Semantic Segmentation

Invertible Rescaling Network and Its Extensions

1 code implementation9 Oct 2022 Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu

To be specific, we develop invertible models to generate valid degraded images and meanwhile transform the distribution of lost contents to the fixed distribution of a latent variable during the forward degradation.

Colorization Image Compression

Time Minimization in Hierarchical Federated Learning

no code implementations7 Oct 2022 Chang Liu, Terence Jie Chua, Jun Zhao

Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts.

Association Federated Learning

Generalized Moving Horizon Estimation for Nonlinear Systems with Robustness to Measurement Outliers

no code implementations5 Oct 2022 Wenhan Cao, Chang Liu, Zhiqian Lan, Yingxi Piao, Shengbo Eben Li

Then we design a robust loss function by leveraging the \{beta}-divergence and propose the \{beta} moving horizon estimator robust to outliers.

Bayesian Inference

Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks

no code implementations26 Sep 2022 Chang Liu, Xuemeng Liu, Shuangyang Li, Weijie Yuan, Derrick Wing Kwan Ng

Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i. e., predicting the angular parameters of users.

CounTR: Transformer-based Generalised Visual Counting

1 code implementation29 Aug 2022 Chang Liu, Yujie Zhong, Andrew Zisserman, Weidi Xie

In this paper, we consider the problem of generalised visual object counting, with the goal of developing a computational model for counting the number of objects from arbitrary semantic categories, using arbitrary number of "exemplars", i. e. zero-shot or few-shot counting.

Object Counting Self-Supervised Learning

Multimodal Transformer for Automatic 3D Annotation and Object Detection

1 code implementation20 Jul 2022 Chang Liu, Xiaoyan Qian, Binxiao Huang, Xiaojuan Qi, Edmund Lam, Siew-Chong Tan, Ngai Wong

By enriching the sparse point clouds, our method achieves 4. 48\% and 4. 03\% better 3D AP on KITTI moderate and hard samples, respectively, versus the state-of-the-art autolabeler.

3D Object Detection object-detection

$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features

no code implementations6 Jul 2022 Zhennan Wang, Kehan Li, Runyi Yu, Yian Zhao, Pengchong Qiao, Chang Liu, Fan Xu, Xiangyang Ji, Guoli Song, Jie Chen

In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.

Acoustic Scene Classification Image Classification +1

An Extendable Maneuver Management Framework with Fault-Tolerant Mechanism for Vehicle Platoon Control System in Highway Scenario

no code implementations4 Jul 2022 Chang Liu, Yugong Luo, Pengfei Li, Chunhui Xing, Weiwei Kong

To deal with this problem, this paper introduces a two-dimensional maneuver management framework with a fault-tolerant mechanism on the basis of the proposed hierarchical architecture for the platoon control system.


TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection

1 code implementation4 Jul 2022 Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang

We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training.

object-detection RGB-D Salient Object Detection +1

Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function

no code implementations25 Jun 2022 HongBing Zhang, Xinyi Liu, Chang Liu, HongTao Fan, YaJing Li, Xinyun Zhu

The proposed function is generalized to tensor cases, yielding tensor MLCP and weighted tensor $L\gamma$-norm.

Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations

no code implementations2 Jun 2022 Chang Liu, Zhen-Hua Ling, Ling-Hui Chen

This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration

Test-time Fourier Style Calibration for Domain Generalization

no code implementations13 May 2022 Xingchen Zhao, Chang Liu, Anthony Sicilia, Seong Jae Hwang, Yun Fu

Thus, it is still possible that those methods can overfit to source domains and perform poorly on target domains.

Domain Generalization

TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

no code implementations28 Apr 2022 Sijia Li, Gaopeng Gou, Chang Liu, Chengshang Hou, Zhenzhen Li, Gang Xiong

In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum.

Representation Learning

Instance-Specific Feature Propagation for Referring Segmentation

no code implementations26 Apr 2022 Chang Liu, Xudong Jiang, Henghui Ding

In this work, we propose a novel framework that simultaneously detects the target-of-interest via feature propagation and generates a fine-grained segmentation mask.

Instance Segmentation Semantic Segmentation

6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning

1 code implementation21 Apr 2022 Tianyu Cui, Gaopeng Gou, Gang Xiong, Chang Liu, Peipei Fu, Zhen Li

6GAN forces multiple generators to train with a multi-class discriminator and an alias detector to generate non-aliased active targets with different addressing pattern types.

Decision Making reinforcement-learning +1

Open-set Text Recognition via Character-Context Decoupling

1 code implementation CVPR 2022 Chang Liu, Chun Yang, Xu-Cheng Yin

Contextual information can be decomposed into temporal information and linguistic information.

Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees

no code implementations6 Apr 2022 Wenhan Cao, Jingliang Duan, Shengbo Eben Li, Chen Chen, Chang Liu, Yu Wang

Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.

MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator

no code implementations29 Mar 2022 Chang Liu, Xiaoyan Qian, Xiaojuan Qi, Edmund Y. Lam, Siew-Chong Tan, Ngai Wong

While a few previous studies tried to automatically generate 3D bounding boxes from weak labels such as 2D boxes, the quality is sub-optimal compared to human annotators.

object-detection Object Detection

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

2 code implementations9 Mar 2022 Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.

Benchmarking Graph Regression

An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets

no code implementations28 Feb 2022 Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.

Time-Frequency Mask Aware Bi-directional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation

no code implementations9 Feb 2022 Jie Chen, Chang Liu, Jiawu Xie, Jie An, Nan Huang

In particular, this method breaks through the limitations of the existing methods, not only achieves good results in multivariate separation, but also effectively separates signals when mixed with 40dB Gaussian noise signals.

Temporal Sequences

Direct Molecular Conformation Generation

1 code implementation3 Feb 2022 Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.

Molecular Docking

Crystal structure prediction with machine learning-based element substitution

1 code implementation26 Jan 2022 Minoru Kusaba, Chang Liu, Ryo Yoshida

The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics.

BIG-bench Machine Learning Metric Learning

DMF-Net: Dual-Branch Multi-Scale Feature Fusion Network for copy forgery identification of anti-counterfeiting QR code

no code implementations19 Jan 2022 Zhongyuan Guo, Hong Zheng, Changhui You, Tianyu Wang, Chang Liu

We first analyze the production principle of anti-counterfeiting QR code, and convert the identification of copy forgery to device category forensics, and then a Dual-Branch Multi-Scale Feature Fusion network is proposed.

Image Forensics

Recovering Latent Causal Factor for Generalization to Distributional Shifts

1 code implementation NeurIPS 2021 Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

To avoid such a spurious correlation, we propose \textbf{La}tent \textbf{C}ausal \textbf{I}nvariance \textbf{M}odels (LaCIM) that specifies the underlying causal structure of the data and the source of distributional shifts, guiding us to pursue only causal factor for prediction.

Feature-Gate Coupling for Dynamic Network Pruning

1 code implementation29 Nov 2021 Mengnan Shi, Chang Liu, Qixiang Ye, Jianbin Jiao

Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of features.

Contrastive Learning Network Pruning

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

no code implementations20 Nov 2021 Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Margarita L. Zuley, Shandong Wu

To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance.

Image Classification Medical Image Classification +1

Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images

no code implementations20 Nov 2021 Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Shandong Wu

These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized.

One-Class Classification

Towards Generating Real-World Time Series Data

1 code implementation16 Nov 2021 Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges.

Time Series Analysis

Decentralized On-Ramp Merging Control of Connected and Automated Vehicles in the Mixed Traffic Using Control Barrier Functions

no code implementations1 Nov 2021 Haoji Liu, Weichao Zhuang, Guodong Yin, Rongcan Li, Chang Liu, Shanxing Zhou

We first formulate the optimal merging control problem, which includes the constraints of safety and vehicle dynamics, with the objectives of minimizing travel time and energy consumption.

Improving Location Recommendation with Urban Knowledge Graph

no code implementations1 Nov 2021 Chang Liu, Chen Gao, Depeng Jin, Yong Li

We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.

Spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020

no code implementations14 Oct 2021 Chang Liu, Hairong Tang, Luyan Ji, Yongchao Zhao

Based on the mapping results, we analyzed the changes of Miyun Reservoir from 1984 to 2020 and the driving factors of them.


Motivating Effort with Information about Future Rewards

no code implementations11 Oct 2021 Chang Liu

The principal knows the reward of the task and provides information to the agent over time.

Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning

no code implementations1 Oct 2021 Chongyang Tao, Jiazhan Feng, Chang Liu, Juntao Li, Xiubo Geng, Daxin Jiang

For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.

Re-Ranking Retrieval

You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors

no code implementations30 Sep 2021 Zijian Zhu, Hang Su, Chang Liu, Wenzhao Xiang, Shibao Zheng

Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.

Self-Driving Cars

Particle Based Stochastic Policy Optimization

no code implementations29 Sep 2021 Qiwei Ye, Yuxuan Song, Chang Liu, Fangyun Wei, Tao Qin, Tie-Yan Liu

Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification.

MuJoCo Games Offline RL +1

Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator

no code implementations13 Sep 2021 Wenzhao Xiang, Hang Su, Chang Liu, Yandong Guo, Shibao Zheng

As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities.

Adversarial Attack

Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

no code implementations26 Aug 2021 Chang Liu, Weijie Yuan, Shuangyang Li, Xuemeng Liu, Husheng Li, Derrick Wing Kwan Ng, Yonghui Li

Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.

Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE

no code implementations ICML Workshop AML 2021 Wenzhao Xiang, Chang Liu, Shibao Zheng

Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm.

Adversarial Attack

Vision-Language Transformer and Query Generation for Referring Segmentation

1 code implementation ICCV 2021 Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang

We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression.

Referring Expression Segmentation

Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering

no code implementations3 Aug 2021 Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao

Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks.

Metric Learning

Tracing Halpha Fibrils through Bayesian Deep Learning

no code implementations16 Jul 2021 Haodi Jiang, Ju Jing, Jiasheng Wang, Chang Liu, Qin Li, Yan Xu, Jason T. L. Wang, Haimin Wang

Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.

Image Segmentation Semantic Segmentation

On the Generative Utility of Cyclic Conditionals

1 code implementation NeurIPS 2021 Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

This is motivated by the observation that deep generative models, in addition to a likelihood model $p(x|z)$, often also use an inference model $q(z|x)$ for extracting representation, but they rely on a usually uninformative prior distribution $p(z)$ to define a joint distribution, which may render problems like posterior collapse and manifold mismatch.

Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks

no code implementations24 Jun 2021 Xianlong Zeng, Simon Lin, Chang Liu

In addition, our framework showed a great generalizability potential to transfer learned knowledge from one institution to another, paving the way for future healthcare model pre-training across institutions.

Transfer Learning

Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods

1 code implementation24 Jun 2021 Xianlong Zeng, Fanghao Song, Zhongen Li, Krerkkiat Chusap, Chang Liu

Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest.

BIG-bench Machine Learning Explainable artificial intelligence

Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis

no code implementations23 Jun 2021 Xianlong Zeng, Simon Lin, Chang Liu

The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level.

Management Representation Learning

Sampling with Mirrored Stein Operators

1 code implementation ICLR 2022 Jiaxin Shi, Chang Liu, Lester Mackey

We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries.

Light Pollution Reduction in Nighttime Photography

no code implementations18 Jun 2021 Chang Liu, Xiaolin Wu

Nighttime photographers are often troubled by light pollution of unwanted artificial lights.

Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography

no code implementations5 Jun 2021 Jingjing Si, Guoliang Li, Yinbo Cheng, Rui Zhang, Godwin Enemali, Chang Liu

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows.

Image Reconstruction

Learning to Route via Theory-Guided Residual Network

no code implementations18 May 2021 Chang Liu, Guanjie Zheng, Zhenhui Li

Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator.

Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach

no code implementations26 Apr 2021 Jie Chen, Jie Liu, Chang Liu, Jian Zhang, Bing Han

To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing.

ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

1 code implementation ICCV 2021 Kai Li, Chang Liu, Handong Zhao, Yulun Zhang, Yun Fu

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.

Data Augmentation Domain Adaptation

Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning

no code implementations6 Apr 2021 Boyu Yang, Mingbao Lin, Binghao Liu, Mengying Fu, Chang Liu, Rongrong Ji, Qixiang Ye

By tentatively expanding network nodes, LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization.

Few-Shot Class-Incremental Learning Incremental Learning

Learning to Simulate on Sparse Trajectory Data

no code implementations22 Mar 2021 Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li

Simulation of the real-world traffic can be used to help validate the transportation policies.

Imitation Learning

AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision

no code implementations22 Mar 2021 Chang Liu, Xiaojuan Qi, Edmund Lam, Ngai Wong

The neuromorphic event cameras, which capture the optical changes of a scene, have drawn increasing attention due to their high speed and low power consumption.

Event-based vision

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

no code implementations17 Mar 2021 Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, Rui Yan

To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).

Representation Learning Retrieval

Ternary Hashing

no code implementations16 Mar 2021 Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang

This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.


Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

2 code implementations CVPR 2021 Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye

Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.

Few-Shot Object Detection object-detection

LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

no code implementations6 Mar 2021 Jeremy Beauchamp, Razvan Bunescu, Cindy Marling, Zhongen Li, Chang Liu

In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future.

Management Time Series Forecasting

Learning Invariant Representations across Domains and Tasks

no code implementations3 Mar 2021 Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, Tie-Yan Liu

Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification.

Domain Adaptation Image Classification +1

Generalizing to Unseen Domains: A Survey on Domain Generalization

1 code implementation2 Mar 2021 Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, Philip S. Yu

Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.

Domain Generalization Out-of-Distribution Generalization +1

Target-Dependent Chemical Species Tomography with Hybrid Meshing of Sensing Regions

no code implementations10 Feb 2021 Rui Zhang, Jingjing Si, Godwin Enemali, Yong Bao, Chang Liu

The proposed scheme was both numerically and experimentally validated using a CST sensor with 32 laser beams using a variety of computational tomographic algorithms.

Towards Enhancing Fine-grained Details for Image Matting

no code implementations22 Jan 2021 Chang Liu, Henghui Ding, Xudong Jiang

In this paper, we argue that recovering these microscopic details relies on low-level but high-definition texture features.

Image Matting

Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

no code implementations20 Jan 2021 Zhuqing Jiang, Chang Liu, Ya'nan Wang, Kai Li, Aidong Men, Haiying Wang, Haiyong Luo

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.

Low-Light Image Enhancement

Non-equilibrium Flux Rope Formation by Confined Flares Preceding a Solar Coronal Mass Ejection

no code implementations6 Jan 2021 Bernhard Kliem, Jeongwoo Lee, Rui Liu, Stephen M. White, Chang Liu, Satoshi Masuda

We present evidence that a magnetic flux rope was formed before a coronal mass ejection (CME) and its associated long-duration flare during a pair of preceding confined eruptions and associated impulsive flares in a compound event in NOAA Active Region 12371.

Solar and Stellar Astrophysics

Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References

no code implementations4 Jan 2021 Ya'nan Wang, Zhuqing Jiang, Chang Liu, Kai Li, Aidong Men, Haiying Wang

This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.

Low-Light Image Enhancement Style Transfer

Neighbor Class Consistency on Unsupervised Domain Adaptation

no code implementations1 Jan 2021 Chang Liu, Kai Li, Yun Fu

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data in a target domain with labeled data from source domain available.

Image Classification Unsupervised Domain Adaptation

Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

2 code implementations16 Dec 2020 Chang Liu, Zetian Jiang, Runzhong Wang, Junchi Yan, Lingxiao Huang, Pinyan Lu

As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.

Combinatorial Optimization Decision Making +3

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization

no code implementations7 Dec 2020 Chang Liu, Yixing Huang, Joscha Maier, Laura Klein, Marc Kachelrieß, Andreas Maier

For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.

Computed Tomography (CT) Image Reconstruction

Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems

1 code implementation1 Dec 2020 Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems.


BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting

no code implementations SEMEVAL 2020 Chang Liu, Dong Yu

We demonstrate the effectiveness of our approaches, which achieves 0. 95 of subtask 1 in F1 while using only a subset of giving training set to fine-tune the BERT model, and our official submission achieves F1 0. 802, which ranks us 16th in the competition.

Common Sense Reasoning Data Augmentation

Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework

no code implementations29 Nov 2020 Yan He, Jifang Qiu, Chang Liu, Yue Liu, Jian Wu

The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC).


Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

no code implementations20 Nov 2020 Godwin Enemali, Rui Zhang, Hugh McCann, Chang Liu

Although a fully parallel data acquisition (DAQ) and signal processing system can achieve these functionalities with maximised temporal response, it leads to a highly complex, expensive and power-consuming instrumentation system with high potential for inconsistency between the sampled beams due to the electronics alone.

Image Reconstruction

Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering

no code implementations19 Nov 2020 Yuanqiang Cai, Chang Liu, Weiqiang Wang, Qixiang Ye

With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications.

Scene Text Detection

Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications

no code implementations10 Nov 2020 Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).

TAG Transfer Learning

Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications

no code implementations10 Nov 2020 Chang Liu, Wenzhong Yan, Ankur Mehta

Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters.


Latent Causal Invariant Model

no code implementations4 Nov 2020 Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.


Learning Causal Semantic Representation for Out-of-Distribution Prediction

1 code implementation NeurIPS 2021 Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output.

Domain Adaptation

CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography

no code implementations8 Oct 2020 Yunfan Jiang, Jingjing Si, Rui Zhang, Godwin Enemali, Bin Zhou, Hugh McCann, Chang Liu

Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e. g. species concentration and temperature, in reactive flows.

Image Reconstruction

Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach

no code implementations16 Sep 2020 Chang Liu, Weijie Yuan, Zhiqiang Wei, Xuemeng Liu, Derrick Wing Kwan Ng

Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.

Attribute-conditioned Layout GAN for Automatic Graphic Design

no code implementations11 Sep 2020 Jianan Li, Jimei Yang, Jianming Zhang, Chang Liu, Christina Wang, Tingfa Xu

In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions.

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

no code implementations11 Sep 2020 Chang Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang

To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.

TAG Transfer Learning

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

no code implementations4 Sep 2020 Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai, Qun Li, Cheng Zhang, Kehua Sheng, Guobin Wu, XiaoHu Qie, Xinbing Wang

Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

1 code implementation3 Sep 2020 Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan

To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy.

Denoising Philosophy

Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

4 code implementations27 Aug 2020 Haodi Jiang, Jiasheng Wang, Chang Liu, Ju Jing, Hao liu, Jason T. L. Wang, Haimin Wang

Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments.

Image Segmentation Semantic Segmentation

SCNet: A Neural Network for Automated Side-Channel Attack

1 code implementation2 Aug 2020 Guanlin Li, Chang Liu, Han Yu, Yanhong Fan, Libang Zhang, Zongyue Wang, Meiqin Wang

Information about system characteristics such as power consumption, electromagnetic leaks and sound can be exploited by the side-channel attack to compromise the system.

Learning to Match Distributions for Domain Adaptation

1 code implementation17 Jul 2020 Chaohui Yu, Jindong Wang, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, Tie-Yan Liu

However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias.

Domain Adaptation Inductive Bias

Discretization-Aware Architecture Search

1 code implementation7 Jul 2020 Yunjie Tian, Chang Liu, Lingxi Xie, Jianbin Jiao, Qixiang Ye

The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods.

Image Classification Neural Architecture Search

Progressive Cluster Purification for Unsupervised Feature Learning

1 code implementation6 Jul 2020 Yifei Zhang, Chang Liu, Yu Zhou, Wei Wang, Weiping Wang, Qixiang Ye

In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.


Modeling Lost Information in Lossy Image Compression

no code implementations22 Jun 2020 Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu

Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored.

Image Compression

Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning

1 code implementation20 Jun 2020 Yuan Yao, Chang Liu, Dezhao Luo, Yu Zhou, Qixiang Ye

The generative perception model acts as a feature decoder to focus on comprehending high temporal resolution and short-term representation by introducing a motion-attention mechanism.

Action Recognition Representation Learning +2

LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation

no code implementations4 Jun 2020 Weihao Jiang, Zhaozhi Xie, Yaoyi Li, Chang Liu, Hongtao Lu

Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network.

Real-Time Semantic Segmentation

Towards Fine-grained Human Pose Transfer with Detail Replenishing Network

no code implementations26 May 2020 Lingbo Yang, Pan Wang, Chang Liu, Zhanning Gao, Peiran Ren, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Xian-Sheng Hua, Wen Gao

Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality.

Pose Transfer Retrieval

HyperSTAR: Task-Aware Hyperparameters for Deep Networks

no code implementations CVPR 2020 Gaurav Mittal, Chang Liu, Nikolaos Karianakis, Victor Fragoso, Mei Chen, Yun Fu

To reduce HPO time, we present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a task-aware method to warm-start HPO for deep neural networks.

Hyperparameter Optimization Image Classification

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

1 code implementation17 May 2020 Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan

We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.

Cross-Lingual Information Retrieval Retrieval

Invertible Image Rescaling

7 code implementations ECCV 2020 Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.

Image Super-Resolution

HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

1 code implementation11 May 2020 Lingbo Yang, Chang Liu, Pan Wang, Shanshe Wang, Peiran Ren, Siwei Ma, Wen Gao

Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents.

Blind Face Restoration Face Hallucination +3

Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

no code implementations8 May 2020 Hao Liu, Yan Xu, Jiasheng Wang, Ju Jing, Chang Liu, Jason T. L. Wang, Haimin Wang

By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS.

Solar and Stellar Astrophysics