Search Results for author: Han Hu

Found 61 papers, 36 papers with code

Scalable Differential Privacy with Certified Robustness in Adversarial Learning

1 code implementation ICML 2020 Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou

In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples.

小样本关系分类研究综述(Few-Shot Relation Classification: A Survey)

no code implementations CCL 2020 Han Hu, Pengyuan Liu

关系分类作为构建结构化知识的重要一环, 在自然语言处理领域备受关注。但在很多应用领域中(医疗、金融领域), 收集充足的用于训练关系分类模型的数据是十分困难的。近年来, 仅需要少量训练样本的小样本学习研究逐渐新兴于各大领域。本文对近期小样本关系分类模型与方法进行了系统的综述。根据度量方法的不同, 将现有方法分为原型式和分布式两大类。根据是否利用额外信息, 将模型分为预训练和非预训练两大类。此外, 除了常规设定下的小样本学习, 本文还梳理了跨领域和稀缺资源场景下的小样本学习, 并探讨了目前小样本关系分类方法的局限性, 分析了跨领域小样本 学习面临的技术挑战。最后, 展望了小样本关系分类未来的发展方向。

Few-Shot Relation Classification

Swin Transformer V2: Scaling Up Capacity and Resolution

3 code implementations18 Nov 2021 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo

Our techniques are generally applicable for scaling up vision models, which has not been widely explored as that of NLP language models, partly due to the following difficulties in training and applications: 1) vision models often face instability issues at scale and 2) many downstream vision tasks require high resolution images or windows and it is not clear how to effectively transfer models pre-trained at low resolutions to higher resolution ones.

 Ranked #1 on Action Classification on Kinetics-400 (using extra training data)

Action Classification Image Classification +3

SimMIM: A Simple Framework for Masked Image Modeling

2 code implementations18 Nov 2021 Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, Han Hu

We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by $40\times$ less data than that in previous practice, we achieve the state-of-the-art on four representative vision benchmarks.

Fine-tuning Representation Learning +1

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

no code implementations17 Nov 2021 Han Hu, Xiaopeng Jiang, Vijaya Datta Mayyuri, An Chen, Devu M. Shila, Adriaan Larmuseau, Ruoming Jin, Cristian Borcea, NhatHai Phan

FLSys is designed to work with mobile sensing data collected on smart phones, balance model performance with resource consumption on the phones, tolerate phone communication failures, and achieve scalability in the cloud.

Activity Recognition Data Augmentation +2

Bootstrap Your Object Detector via Mixed Training

1 code implementation NeurIPS 2021 Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai

We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free.

Data Augmentation Object Detection

Joint Task Offloading and Resource Allocation for IoT Edge Computing with Sequential Task Dependency

no code implementations23 Oct 2021 Xuming An, Rongfei Fan, Han Hu, Ning Zhang, Saman Atapattu, Theodoros A. Tsiftsis

To solve this challenging problem, we decompose it as a one-dimensional search of task offloading decision problem and a non-convex optimization problem with task offloading decision given.

Edge-computing

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning

1 code implementation NeurIPS 2021 Hanzhe Hu, Fangyun Wei, Han Hu, Qiwei Ye, Jinshi Cui, LiWei Wang

The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling.

Data Augmentation Semi-Supervised Semantic Segmentation

User-Entity Differential Privacy in Learning Natural Language Models

no code implementations29 Sep 2021 Phung Lai, Tong Sun, Rajiv Jain, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Han Hu, Hai Phan

In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models.

Energy-Efficient Design for IRS-Assisted MEC Networks with NOMA

no code implementations19 Sep 2021 Qun Wang, Fuhui Zhou, Han Hu, Rose Qingyang Hu

Energy-efficient design is of crucial importance in wireless internet of things (IoT) networks.

Edge-computing

Video Swin Transformer

5 code implementations24 Jun 2021 Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, Han Hu

The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.

 Ranked #1 on Action Recognition on Something-Something V2 (using extra training data)

Action Classification Action Recognition +3

Aligning Pretraining for Detection via Object-Level Contrastive Learning

1 code implementation NeurIPS 2021 Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin

Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning.

Contrastive Learning Object Detection +3

TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

no code implementations27 May 2021 Kaixin Zhang, Hongzhi Wang, Tongxin Li, Han Hu, Songling Zou, Jiye Qiu

In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, with taking the multiple dynamic workloads into consideration.

Group-Free 3D Object Detection via Transformers

2 code implementations ICCV 2021 Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong

Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training.

3D Object Detection

Capsule Network is Not More Robust than Convolutional Network

no code implementations CVPR 2021 Jindong Gu, Volker Tresp, Han Hu

The examination reveals five major new/different components in CapsNet: a transformation process, a dynamic routing layer, a squashing function, a marginal loss other than cross-entropy loss, and an additional class-conditional reconstruction loss for regularization.

Affine Transformation Image Classification

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

34 code implementations ICCV 2021 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.

Ranked #3 on Semantic Segmentation on FoodSeg103 (using extra training data)

Image Classification Instance Segmentation +2

Boosting Adversarial Transferability through Enhanced Momentum

no code implementations19 Mar 2021 Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, Kun He

Various momentum iterative gradient-based methods are shown to be effective to improve the adversarial transferability.

Adversarial Attack

Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks

no code implementations16 Mar 2021 Han Hu, Weiwei Song, Qun Wang, Fuhui Zhou, Rose Qingyang Hu

In this paper, the offloading decision and resource allocation problem is studied with mobility consideration.

Autonomous Driving Edge-computing

Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network

no code implementations9 Feb 2021 Qun Wang, Han Hu, Haijian Sun, Rose Qingyang Hu

In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations.

Edge-computing

Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps

1 code implementation12 Jan 2021 Yujin Huang, Han Hu, Chunyang Chen

Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition.

Adversarial Attack Fine-tuning +3

Global Context Networks

3 code implementations24 Dec 2020 Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, via aggregating query-specific global context to each query position.

Instance Segmentation

Evading Web Application Firewalls with Reinforcement Learning

no code implementations CUHK Course IERG5350 2020 Xianbo Wang, Han Hu

Our framework successfully discovered numbers of evasion payloads for each WAF in our experiments and can significantly outperform baseline policy.

OpenAI Gym

Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images

no code implementations26 Nov 2020 Qing Zhu, Shengzhi Huang, Han Hu, Haifeng Li, Min Chen, Ruofei Zhong

Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings.

Joint Task Offloading and Allocation of Communication and Computation Resources for Energy-Efficient Mobile Edge Computing with Sequential Task Dependency

no code implementations25 Nov 2020 Xuming An, Rongfei Fan, Han Hu, Ning Zhang, Saman Atapattu, Theodoros A. Tsiftsis

To solve this challenging problem, we decompose it as a one-dimensional search of task offloading decision problem and a non-convex optimization problem with task offloading decision given.

Edge-computing Information Theory Information Theory

Structure-Aware Completion of Photogrammetric Meshes in Urban Road Environment

1 code implementation23 Nov 2020 Qing Zhu, Qisen Shang, Han Hu, Haojia Yu, Ruofei Zhong

Finally, the completed rendered image is deintegrated to the original texture atlas and the triangles for the vehicles are also flattened for improved meshes.

Object Detection

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

5 code implementations CVPR 2021 Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, Han Hu

We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions.

Contrastive Learning Object Detection +2

RepPoints V2: Verification Meets Regression for Object Detection

1 code implementation NeurIPS 2020 Yihong Chen, Zheng Zhang, Yue Cao, Li-Wei Wang, Stephen Lin, Han Hu

Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement.

Instance Segmentation Object Detection +2

A Closer Look at Local Aggregation Operators in Point Cloud Analysis

1 code implementation ECCV 2020 Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong

Our investigation reveals that despite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks.

3D Semantic Segmentation

Disentangled Non-Local Neural Networks

4 code implementations ECCV 2020 Minghao Yin, Zhuliang Yao, Yue Cao, Xiu Li, Zheng Zhang, Stephen Lin, Han Hu

This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel.

Action Recognition Object Detection +1

Ontology-based Interpretable Machine Learning for Textual Data

2 code implementations1 Apr 2020 Phung Lai, NhatHai Phan, Han Hu, Anuja Badeti, David Newman, Dejing Dou

In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models.

Interpretable Machine Learning

Memory Enhanced Global-Local Aggregation for Video Object Detection

2 code implementations CVPR 2020 Yihong Chen, Yue Cao, Han Hu, Li-Wei Wang

We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.

Video Object Detection

Fast and Regularized Reconstruction of Building Façades from Street-View Images using Binary Integer Programming

1 code implementation20 Feb 2020 Han Hu, Libin Wang, Mier Zhang, Yulin Ding, Qing Zhu

Regularized arrangement of primitives on building fa\c{c}ades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment.

3D Reconstruction

Deep Fusion of Local and Non-Local Features for Precision Landslide Recognition

1 code implementation20 Feb 2020 Qing Zhu, Lin Chen, Han Hu, Binzhi Xu, Yeting Zhang, Haifeng Li

The second uses a scale attention mechanism to guide the up-sampling of features from the coarse level by a learned weight map.

Semantic Segmentation

Dense RepPoints: Representing Visual Objects with Dense Point Sets

2 code implementations ECCV 2020 Ze Yang, Yinghao Xu, Han Xue, Zheng Zhang, Raquel Urtasun, Li-Wei Wang, Stephen Lin, Han Hu

We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.

Object Detection

MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery

1 code implementation26 Oct 2019 Qing Zhu, Cheng Liao, Han Hu, Xiaoming Mei, Haifeng Li

This paper proposes a novel multi attending path neural network (MAP-Net) for accurately extracting multiscale building footprints and precise boundaries.

Differential Privacy in Adversarial Learning with Provable Robustness

no code implementations25 Sep 2019 NhatHai Phan, My T. Thai, Ruoming Jin, Han Hu, Dejing Dou

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.

XCMRC: Evaluating Cross-lingual Machine Reading Comprehension

no code implementations15 Aug 2019 Pengyuan Liu, Yuning Deng, Chenghao Zhu, Han Hu

Chinese and English are rich-resource language pairs, in order to study low-resource cross-lingual machine reading comprehension (XMRC), besides defining the common XCMRC task which has no restrictions on use of external language resources, we also define the pseudo low-resource XCMRC task by limiting the language resources to be used.

Language understanding Machine Reading Comprehension

Spatial-Temporal Relation Networks for Multi-Object Tracking

no code implementations ICCV 2019 Jiarui Xu, Yue Cao, Zheng Zhang, Han Hu

Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers.

Multi-Object Tracking Multiple Object Tracking

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

9 code implementations25 Apr 2019 Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu

In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation.

Instance Segmentation Object Detection +1

Preserving Differential Privacy in Adversarial Learning with Provable Robustness

no code implementations23 Mar 2019 NhatHai Phan, My T. Thai, Ruoming Jin, Han Hu, Dejing Dou

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.

Cryptography and Security

Deep Metric Transfer for Label Propagation with Limited Annotated Data

1 code implementation20 Dec 2018 Bin Liu, Zhirong Wu, Han Hu, Stephen Lin

In this paper, we propose a generic framework that utilizes unlabeled data to aid generalization for all three tasks.

Metric Learning Object Recognition +1

Deformable ConvNets v2: More Deformable, Better Results

18 code implementations CVPR 2019 Xizhou Zhu, Han Hu, Stephen Lin, Jifeng Dai

The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.

Instance Segmentation Object Detection +1

Learning Region Features for Object Detection

no code implementations ECCV 2018 Jiayuan Gu, Han Hu, Li-Wei Wang, Yichen Wei, Jifeng Dai

While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods.

Object Detection

Relation Networks for Object Detection

5 code implementations CVPR 2018 Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.

Object Detection Object Recognition

Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

2 code implementations18 Sep 2017 NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou

In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks.

WordSup: Exploiting Word Annotations for Character based Text Detection

no code implementations ICCV 2017 Han Hu, Chengquan Zhang, Yuxuan Luo, Yuzhuo Wang, Junyu Han, Errui Ding

When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e. g. ICDAR15 and COCO-text.

Scene Text Scene Text Detection

Deformable Convolutional Networks

35 code implementations ICCV 2017 Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.

Object Detection Semantic Segmentation

Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM

no code implementations15 Aug 2016 Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang

Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%.

Classification General Classification +2

Smooth Representation Clustering

no code implementations CVPR 2014 Han Hu, Zhouchen Lin, Jianjiang Feng, Jie zhou

Based on our analysis, we propose the SMooth Representation (SMR) model.

Pose from Flow and Flow from Pose

no code implementations CVPR 2013 Katerina Fragkiadaki, Han Hu, Jianbo Shi

The pose labeled segments and corresponding articulated joints are used to improve the motion flow fields by proposing kinematically constrained affine displacements on body parts.

Motion Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.