Search Results for author: Shuang Li

Found 106 papers, 53 papers with code

Temporal Logic Point Processes

no code implementations ICML 2020 Shuang Li, Lu Wang, Ruizhi Zhang, xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song

We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.

Point Processes

A Function Interpretation Benchmark for Evaluating Interpretability Methods

1 code implementation7 Sep 2023 Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba

Labeling neural network submodules with human-legible descriptions is useful for many downstream tasks: such descriptions can surface failures, guide interventions, and perhaps even explain important model behaviors.

Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models

no code implementations26 Aug 2023 Shuang Li, Jiangjie Chen, Siyu Yuan, Xinyi Wu, Hao Yang, Shimin Tao, Yanghua Xiao

To translate well, machine translation (MT) systems and general-purposed language models (LMs) need a deep understanding of both source and target languages and cultures.

Machine Translation Translation

Hawkes Processes with Delayed Granger Causality

no code implementations11 Aug 2023 Chao Yang, Hengyuan Miao, Shuang Li

We aim to explicitly model the delayed Granger causal effects based on multivariate Hawkes processes.

Reinforcement Logic Rule Learning for Temporal Point Processes

no code implementations11 Aug 2023 Chao Yang, Lu Wang, Kun Gao, Shuang Li

Leveraging the temporal point process modeling and learning framework, the rule content and weights will be gradually optimized until the likelihood of the observational event sequences is optimal.

Point Processes

Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions

no code implementations21 Jun 2023 Chengzhi Cao, Chao Yang, Shuang Li

Our approach is inspired by the fact that human actions are usually driven by their intentions or desires, and are influenced by environmental factors such as the spatial relationships with surrounding objects.

Sports Analytics

Variational Disentangled Graph Auto-Encoders for Link Prediction

no code implementations20 Jun 2023 Jun Fu, Xiaojuan Zhang, Shuang Li, Dali Chen

The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors, which contributes to improving the performance of link prediction.

Disentanglement Link Prediction

Contrastive Disentangled Learning on Graph for Node Classification

no code implementations20 Jun 2023 Xiaojuan Zhang, Jun Fu, Shuang Li

Inspired by the success of contrastive learning, we propose a novel framework for contrastive disentangled learning on graphs, employing a disentangled graph encoder and two carefully crafted self-supervision signals.

Classification Contrastive Learning +3

Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models

no code implementations8 Jun 2023 Nan Liu, Yilun Du, Shuang Li, Joshua B. Tenenbaum, Antonio Torralba

Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate.

Image Generation

Stochastic Natural Thresholding Algorithms

no code implementations7 Jun 2023 Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell, Jing Qin

Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing.

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

no code implementations29 May 2023 Aiwei Liu, Wei Liu, Xuming Hu, Shuang Li, Fukun Ma, Yawen Yang, Lijie Wen

Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models.

SQL Parsing Text-To-SQL

Improving Factuality and Reasoning in Language Models through Multiagent Debate

1 code implementation23 May 2023 Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch

Our findings indicate that this approach significantly enhances mathematical and strategic reasoning across a number of tasks.

Few-Shot Learning Language Modelling +1

Introspective Tips: Large Language Model for In-Context Decision Making

no code implementations19 May 2023 Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang, Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.

Decision Making Language Modelling +2

Robust Test-Time Adaptation in Dynamic Scenarios

1 code implementation CVPR 2023 Longhui Yuan, Binhui Xie, Shuang Li

Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams.

Autonomous Driving

Improving Generalization with Domain Convex Game

1 code implementation CVPR 2023 Fangrui Lv, Jian Liang, Shuang Li, Jinming Zhang, Di Liu

A classical solution to DG is domain augmentation, the common belief of which is that diversifying source domains will be conducive to the out-of-distribution generalization.

Domain Generalization Out-of-Distribution Generalization

Open-vocabulary Panoptic Segmentation with Embedding Modulation

no code implementations20 Mar 2023 Xi Chen, Shuang Li, Ser-Nam Lim, Antonio Torralba, Hengshuang Zhao

Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world.

Image Segmentation Open Vocabulary Panoptic Segmentation +1

Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

1 code implementation27 Feb 2023 Mixue Xie, Shuang Li, Rui Zhang, Chi Harold Liu

Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue.

Active Learning Domain Adaptation +2

ConceptFusion: Open-set Multimodal 3D Mapping

no code implementations14 Feb 2023 Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd Omama, Tao Chen, Shuang Li, Ganesh Iyer, Soroush Saryazdi, Nikhil Keetha, Ayush Tewari, Joshua B. Tenenbaum, Celso Miguel de Melo, Madhava Krishna, Liam Paull, Florian Shkurti, Antonio Torralba

ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.

Autonomous Driving Robot Navigation

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

1 code implementation22 Nov 2022 Mingjia Li, Binhui Xie, Shuang Li, Chi Harold Liu, Xinjing Cheng

However, previous methods often reckon on additional reference images of the same scenes taken from normal conditions, which are quite tough to collect in reality.

Domain Adaptation Semantic Segmentation

Joint Semantic Transfer Network for IoT Intrusion Detection

no code implementations28 Oct 2022 Jiashu Wu, Yang Wang, Binhui Xie, Shuang Li, Hao Dai, Kejiang Ye, Chengzhong Xu

The scenario semantic endows source NI and II domain with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation.

Domain Adaptation Intrusion Detection +2

Composing Ensembles of Pre-trained Models via Iterative Consensus

no code implementations20 Oct 2022 Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba, Igor Mordatch

Such closed-loop communication enables models to correct errors caused by other models, significantly boosting performance on downstream tasks, e. g. improving accuracy on grade school math problems by 7. 5%, without requiring any model finetuning.

 Ranked #1 on Video Question Answering on ActivityNet-QA (Vocabulary Size metric)

Image Generation Mathematical Reasoning +2

FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

1 code implementation18 Oct 2022 Decheng Liu, Zhan Dang, Chunlei Peng, Yu Zheng, Shuang Li, Nannan Wang, Xinbo Gao

Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery.

Federated Learning Image Generation

Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification

1 code implementation CVPR 2022 Xinyu Lin, Jinxing Li, Zeyu Ma, Huafeng Li, Shuang Li, Kaixiong Xu, Guangming Lu, David Zhang

Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID.

Cross-Modal Retrieval Person Re-Identification +1

Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation

1 code implementation2 Aug 2022 Wenxuan Ma, Jinming Zhang, Shuang Li, Chi Harold Liu, Yulin Wang, Wei Li

To alleviate these issues, we propose to simultaneously conduct feature alignment in two individual spaces focusing on different domains, and create for each space a domain-oriented classifier tailored specifically for that domain.

Pseudo Label Unsupervised Domain Adaptation

SP2: A Second Order Stochastic Polyak Method

no code implementations17 Jul 2022 Shuang Li, William J. Swartworth, Martin Takáč, Deanna Needell, Robert M. Gower

We take a step further and develop a method for solving the interpolation equations that uses the local second-order approximation of the model.

Matrix Completion Second-order methods

Learning Iterative Reasoning through Energy Minimization

1 code implementation30 Jun 2022 Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch

Finally, we illustrate that our approach can recursively solve algorithmic problems requiring nested reasoning

Image Classification Object Recognition

Compositional Visual Generation with Composable Diffusion Models

1 code implementation3 Jun 2022 Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, Joshua B. Tenenbaum

Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions.

Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?

no code implementations28 May 2022 Shuang Li, Ke Li, Wei Li

Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems.

Improving Transferability for Domain Adaptive Detection Transformers

1 code implementation29 Apr 2022 Kaixiong Gong, Shuang Li, Shugang Li, Rui Zhang, Chi Harold Liu, Qiang Chen

We implement the findings and the alignment modules into our adaptation method, and it benchmarks the DETR-style detector on the domain shift settings.

Object Detection Unsupervised Domain Adaptation

ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation

no code implementations26 Apr 2022 Zhenjie Yu, Kai Chen, Shuang Li, Bingfeng Han, Chi Harold Liu, Shuigen Wang

To be specific, ROMA could efficiently translate the unpaired nighttime infrared videos into fine-grained daytime visible ones, meanwhile maintain the spatiotemporal consistency via matching the cross-domain region similarity.


SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

1 code implementation19 Apr 2022 Binhui Xie, Shuang Li, Mingjia Li, Chi Harold Liu, Gao Huang, Guoren Wang

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain.

Semantic Segmentation Synthetic-to-Real Translation

Causality Inspired Representation Learning for Domain Generalization

1 code implementation CVPR 2022 Fangrui Lv, Jian Liang, Shuang Li, Bin Zang, Chi Harold Liu, Ziteng Wang, Di Liu

Specifically, we assume that each input is constructed from a mix of causal factors (whose relationship with the label is invariant across domains) and non-causal factors (category-independent), and only the former cause the classification judgments.

Domain Generalization Representation Learning

Domain Adaptation via Prompt Learning

1 code implementation14 Feb 2022 Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, Gao Huang

Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given.

Domain Adaptation

Learning Temporal Rules from Noisy Timeseries Data

no code implementations11 Feb 2022 Karan Samel, Zelin Zhao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan Essa, Le Song

Events across a timeline are a common data representation, seen in different temporal modalities.

Pre-Trained Language Models for Interactive Decision-Making

1 code implementation3 Feb 2022 Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu

Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans; these representations can aid learning and generalization even outside of language processing.

Imitation Learning Language Modelling

Mars Entry Trajectory Planning with Range Discretization and Successive Convexification

no code implementations24 Jan 2022 Xu Liu, Shuang Li, Ming Xin

The virtual control and adaptive trust-region techniques are employed to improve the accuracy, robustness, and computation efficiency of the algorithm.

Numerical Integration Trajectory Planning

Fast solver for J2-perturbed Lambert problem using deep neural network

no code implementations9 Jan 2022 Bin Yang, Shuang Li, Jinglang Feng, Massimiliano Vasile

The intelligent initial guess generator is a deep neural network that is trained to correct the initial velocity vector coming from the solution of the unperturbed Lambert problem.

Pareto Domain Adaptation

1 code implementation NeurIPS 2021 Fangrui Lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang

Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source.

Domain Adaptation Image Classification +2

Incentive Compatible Pareto Alignment for Multi-Source Large Graphs

1 code implementation6 Dec 2021 Jian Liang, Fangrui Lv, Di Liu, Zehui Dai, Xu Tian, Shuang Li, Fei Wang, Han Li

Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data.

Active Learning for Domain Adaptation: An Energy-Based Approach

1 code implementation2 Dec 2021 Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, Guoren Wang

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains.

Active Learning Transfer Learning +1

SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning

1 code implementation24 Nov 2021 Binhui Xie, Mingjia Li, Shuang Li

Although there is significant progress in supervised semantic segmentation, it remains challenging to deploy the segmentation models to unseen domains due to domain biases.

Contrastive Learning Domain Adaptation +3

Learning to Compose Visual Relations

no code implementations NeurIPS 2021 Nan Liu, Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba

The visual world around us can be described as a structured set of objects and their associated relations.

Unsupervised Learning of Compositional Energy Concepts

1 code implementation NeurIPS 2021 Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch

In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well as objects under a unified framework.

Disentanglement Unsupervised Image Decomposition

Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions

1 code implementation ICCV 2021 Shuang Li, Yilun Du, Antonio Torralba, Josef Sivic, Bryan Russell

Our task poses unique challenges as a system does not know what types of human-object interactions are present in a video or the actual spatiotemporal location of the human and the object.

Human-Object Interaction Detection Weakly-supervised Learning

Language Model Pre-training Improves Generalization in Policy Learning

no code implementations29 Sep 2021 Shuang Li, Xavier Puig, Yilun Du, Ekin Akyürek, Antonio Torralba, Jacob Andreas, Igor Mordatch

Additional experiments explore the role of language-based encodings in these results; we find that it is possible to train a simple adapter layer that maps from observations and action histories to LM embeddings, and thus that language modeling provides an effective initializer even for tasks with no language as input or output.

Imitation Learning Language Modelling

Neural Temporal Logic Programming

no code implementations29 Sep 2021 Karan Samel, Zelin Zhao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan Essa, Le Song

Events across a timeline are a common data representation, seen in different temporal modalities.

Semantic Concentration for Domain Adaptation

1 code implementation ICCV 2021 Shuang Li, Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, Wei Li

To tackle this issue, we propose Semantic Concentration for Domain Adaptation (SCDA), which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions.

Domain Adaptation Transfer Learning

ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach

no code implementations8 Aug 2021 Qi Wen, Shuang Li, Bingfeng Han, Yi Yuan

Chinese character style transfer is a very challenging problem because of the complexity of the glyph shapes or underlying structures and large numbers of existed characters, when comparing with English letters.

Font Generation Style Transfer

I2V-GAN: Unpaired Infrared-to-Visible Video Translation

1 code implementation2 Aug 2021 Shuang Li, Bingfeng Han, Zhenjie Yu, Chi Harold Liu, Kai Chen, Shuigen Wang

Human vision is often adversely affected by complex environmental factors, especially in night vision scenarios.

object-detection Object Detection +1

Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View

no code implementations24 Jun 2021 Shuang Li, Lu Wang, Xinyun Chen, Yixiang Fang, Yan Song

In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease.

Imitation Learning Point Processes

Quotient Space-Based Keyword Retrieval in Sponsored Search

no code implementations26 May 2021 Yijiang Lian, Shuang Li, Chaobing Feng, Yanfeng Zhu

Since the synonymous relations between queries and keywords are quite scarce, the traditional information retrieval framework is inefficient in this scenario.

Information Retrieval Retrieval

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

1 code implementation17 May 2021 Lu Wang, xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei zhang, Xiaofeng He, Le Song, Jingren Zhou, Hongxia Yang

Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.

Contrastive Learning Graph Learning +2

Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation

1 code implementation11 May 2021 Shuang Li, Binhui Xie, Bin Zang, Chi Harold Liu, Xinjing Cheng, Ruigang Yang, Guoren Wang

Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains.

Self-Supervised Learning Semantic Segmentation

Dynamic Domain Adaptation for Efficient Inference

1 code implementation CVPR 2021 Shuang Li, Jinming Zhang, Wenxuan Ma, Chi Harold Liu, Wei Li

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy.

Domain Generalization Transfer Learning

Generalized Domain Conditioned Adaptation Network

1 code implementation23 Mar 2021 Shuang Li, Binhui Xie, Qiuxia Lin, Chi Harold Liu, Gao Huang, Guoren Wang

Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision.

Domain Adaptation

Transferable Semantic Augmentation for Domain Adaptation

1 code implementation CVPR 2021 Shuang Li, Mixue Xie, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Wei Li

To remedy this, we propose a Transferable Semantic Augmentation (TSA) approach to enhance the classifier adaptation ability through implicitly generating source features towards target semantics.

Domain Adaptation

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

1 code implementation CVPR 2021 Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng

Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes.

Data Augmentation Image Classification +2

Digital Beamforming Robust to Time-Varying Carrier Frequency Offset

no code implementations8 Mar 2021 Shuang Li, Payam Nayeri, Michael B. Wakin

We present novel beamforming algorithms that are robust to signal corruptions arising from this time-variant carrier frequency offset.

One-shot Face Reenactment Using Appearance Adaptive Normalization

no code implementations8 Feb 2021 Guangming Yao, Yi Yuan, Tianjia Shao, Shuang Li, Shanqi Liu, Yong liu, Mengmeng Wang, Kun Zhou

The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance.

Face Reenactment

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

1 code implementation13 Dec 2020 Shuang Li, Fangrui Lv, Binhui Xie, Chi Harold Liu, Jian Liang, Chen Qin

Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability.

Semantic Segmentation

Improved Contrastive Divergence Training of Energy Based Models

4 code implementations2 Dec 2020 Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch

Contrastive divergence is a popular method of training energy-based models, but is known to have difficulties with training stability.

Data Augmentation Image Generation +1

Compositional Visual Generation with Energy Based Models

no code implementations NeurIPS 2020 Yilun Du, Shuang Li, Igor Mordatch

A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge.

SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition

1 code implementation CVPR 2021 Yan Xia, Yusheng Xu, Shuang Li, Rui Wang, Juan Du, Daniel Cremers, Uwe Stilla

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors.

Metric Learning Point Cloud Retrieval +1

Energy-Based Models for Continual Learning

1 code implementation24 Nov 2020 Shuang Li, Yilun Du, Gido M. van de Ven, Igor Mordatch

We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems.

Continual Learning

Domain Agnostic Learning for Unbiased Authentication

no code implementations11 Oct 2020 Jian Liang, Yuren Cao, Shuang Li, Bing Bai, Hao Li, Fei Wang, Kun Bai

We further extend our method to a meta-learning framework to pursue more thorough domain-difference elimination.

Face Recognition Meta-Learning +1

Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation

1 code implementation4 Aug 2020 Shuang Li, Binhui Xie, Jiashu Wu, Ying Zhao, Chi Harold Liu, Zhengming Ding

In this paper, we propose a Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains.

Domain Adaptation Pseudo Label

Domain Conditioned Adaptation Network

1 code implementation14 May 2020 Shuang Li, Chi Harold Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang

Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target.

Domain Adaptation

Compositional Visual Generation and Inference with Energy Based Models

1 code implementation13 Apr 2020 Yilun Du, Shuang Li, Igor Mordatch

A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge.

Deep Residual Correction Network for Partial Domain Adaptation

1 code implementation10 Apr 2020 Shuang Li, Chi Harold Liu, Qiuxia Lin, Qi Wen, Limin Su, Gao Huang, Zhengming Ding

Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain.

Partial Domain Adaptation

A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU

1 code implementation11 Mar 2020 Shuang Li, Jiaxi Jiang, Philipp Ruppel, Hongzhuo Liang, Xiaojian Ma, Norman Hendrich, Fuchun Sun, Jianwei Zhang

In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking method.

Anatomy Image-to-Image Translation +1

Robust Robotic Pouring using Audition and Haptics

1 code implementation29 Feb 2020 Hongzhuo Liang, Chuangchuang Zhou, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Fuchun Sun, Marcus Stoffel, Jianwei Zhang

Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment.

Iterative Hard Thresholding for Low CP-rank Tensor Models

no code implementations22 Aug 2019 Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell, Jing Qin

In this paper, we utilize the same tensor version of the Restricted Isometry Property (RIP) to extend these results for tensors with low CANDECOMP/PARAFAC (CP) rank.

The Landscape of Non-convex Empirical Risk with Degenerate Population Risk

no code implementations NeurIPS 2019 Shuang Li, Gongguo Tang, Michael B. Wakin

We also apply the theory to matrix sensing and phase retrieval to demonstrate how to infer the landscape of empirical risk from that of the corresponding population risk.

Matrix Completion Retrieval

Imitation Learning of Neural Spatio-Temporal Point Processes

1 code implementation13 Jun 2019 Shixiang Zhu, Shuang Li, Zhigang Peng, Yao Xie

We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinforcement learning) based approach for model fitting.

Imitation Learning Point Processes

An Approach for Process Model Extraction By Multi-Grained Text Classification

1 code implementation16 May 2019 Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shuang Li, Jian-Min Wang

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.

General Classification Management +5

Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring

1 code implementation2 Mar 2019 Hongzhuo Liang, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Jianwei Zhang

PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task.

Robotics Sound Audio and Speech Processing

Learning Temporal Point Processes via Reinforcement Learning

no code implementations NeurIPS 2018 Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.

Point Processes reinforcement-learning +1

Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network

4 code implementations17 Sep 2018 Shuang Li, Xiaojian Ma, Hongzhuo Liang, Michael Görner, Philipp Ruppel, Bing Fang, Fuchun Sun, Jianwei Zhang

In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands.


PointNetGPD: Detecting Grasp Configurations from Point Sets

4 code implementations17 Sep 2018 Hongzhuo Liang, Xiaojian Ma, Shuang Li, Michael Görner, Song Tang, Bin Fang, Fuchun Sun, Jianwei Zhang

In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.


Question-Guided Hybrid Convolution for Visual Question Answering

no code implementations ECCV 2018 Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven Hoi, Xiaogang Wang

Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features. To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage.

Question Answering Visual Question Answering

Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification

no code implementations CVPR 2018 Shuang Li, Slawomir Bak, Peter Carr, Xiaogang Wang

As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence.

Video-Based Person Re-Identification

Can Image Retrieval help Visual Saliency Detection?

no code implementations24 Sep 2017 Shuang Li, Peter Mathews

We propose a novel image retrieval framework for visual saliency detection using information about salient objects contained within bounding box annotations for similar images.

Image Retrieval Retrieval +1

Fake News Mitigation via Point Process Based Intervention

no code implementations ICML 2017 Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.

reinforcement-learning Reinforcement Learning (RL)

Person Search with Natural Language Description

1 code implementation CVPR 2017 Shuang Li, Tong Xiao, Hongsheng Li, Bolei Zhou, Dayu Yue, Xiaogang Wang

Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance.

Person Search Text based Person Retrieval

Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits

no code implementations14 Oct 2016 Shuang Li, Yao Xie, Le Song

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence.

Change Point Detection

Detecting weak changes in dynamic events over networks

no code implementations29 Mar 2016 Shuang Li, Yao Xie, Mehrdad Farajtabar, Apurv Verma, Le Song

Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics.

Change Point Detection Point Processes

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

no code implementations NeurIPS 2015 Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time.

M-Statistic for Kernel Change-Point Detection

no code implementations NeurIPS 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning.

Change Point Detection

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

1 code implementation NeurIPS 2015 Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it.

Scan $B$-Statistic for Kernel Change-Point Detection

no code implementations5 Jul 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

A novel theoretical result of the paper is the characterization of the tail probability of these statistics using the change-of-measure technique, which focuses on characterizing the tail of the detection statistics rather than obtaining its asymptotic distribution under the null distribution.

Change Point Detection

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