Search Results for author: Cheng Deng

Found 67 papers, 25 papers with code

Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph

no code implementations14 Apr 2023 Cheng Deng, Jiaxin Ding, Luoyi Fu, Weinan Zhang, Xinbing Wang, Chenghu Zhou

In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.

Classification Contrastive Learning +2

FMGNN: Fused Manifold Graph Neural Network

no code implementations3 Apr 2023 Cheng Deng, Fan Xu, Jiaxing Ding, Luoyi Fu, Weinan Zhang, Xinbing Wang

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks.

Graph Representation Learning Link Prediction +1

PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

2 code implementations2 Apr 2023 Cheng Deng, Bo Tong, Luoyi Fu, Jiaxin Ding, Dexing Cao, Xinbing Wang, Chenghu Zhou

In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.

Knowledge Graphs Language Modelling

Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery

1 code implementation CVPR 2023 Muli Yang, Liancheng Wang, Cheng Deng, Hanwang Zhang

Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes.

Novel Class Discovery

Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder

no code implementations CVPR 2023 Aming Wu, Cheng Deng

To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe deployment of object detectors.

object-detection Object Detection +1

Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification

no code implementations20 Nov 2022 Wenli Sun, Xinyang Jiang, Shuguang Dou, Dongsheng Li, Duoqian Miao, Cheng Deng, Cairong Zhao

Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities.

Backdoor Attack Image Steganography +2

Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning

1 code implementation CVPR 2022 Xiangyu Li, Xu Yang, Kun Wei, Cheng Deng, Muli Yang

Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets.

Compositional Zero-Shot Learning

Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm

no code implementations19 Mar 2022 Qingsong Zhang, Bin Gu, Zhiyuan Dang, Cheng Deng, Heng Huang

Based on that, we propose a novel and practical VFL framework with black-box models, which is inseparably interconnected to the promising properties of ZOO.

Federated Learning

Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency

1 code implementation CVPR 2022 Yanan Gu, Xu Yang, Kun Wei, Cheng Deng

Unfortunately, these methods only focus on selecting samples from the memory bank for replay and ignore the adequate exploration of semantic information in the single-pass data stream, leading to poor classification accuracy.

Continual Learning

Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation

1 code implementation CVPR 2022 Aming Wu, Cheng Deng

Particularly, for the night-sunny scene, our method outperforms baselines by 3%, which indicates that our method is instrumental in enhancing generalization ability.

object-detection Object Detection

Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery

1 code implementation CVPR 2022 Muli Yang, Yuehua Zhu, Jiaping Yu, Aming Wu, Cheng Deng

In response to the explosively-increasing requirement of annotated data, Novel Class Discovery (NCD) has emerged as a promising alternative to automatically recognize unknown classes without any annotation.

Novel Class Discovery

Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning

no code implementations CVPR 2022 Jiexi Yan, Lei Luo, Chenghao Xu, Cheng Deng, Heng Huang

While in metric space, we utilize weakly-supervised contrastive learning to excavate these negative correlations hidden in noisy data.

Contrastive Learning

Mutual Quantization for Cross-Modal Search With Noisy Labels

no code implementations CVPR 2022 Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng

More specifically, we propose a proxy-based contrastive (PC) loss to mitigate the gap between different modalities and train networks for different modalities jointly with small-loss samples that are selected with the PC loss and a mutual quantization loss.


Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement

1 code implementation NeurIPS 2021 Aming Wu, Suqi Zhao, Cheng Deng, Wei Liu

To alleviate the impact of few samples, enhancing the generalization and discrimination abilities of detectors on new objects plays an important role.

Dictionary Learning Few-Shot Object Detection +1

Adaptive Hierarchical Similarity Metric Learning with Noisy Labels

no code implementations29 Oct 2021 Jiexi Yan, Lei Luo, Cheng Deng, Heng Huang

Since these noisy labels often cause severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML.

Learning with noisy labels Metric Learning

Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer

no code implementations29 Sep 2021 Ziqi Zhang, Cheng Deng, Kun Wei, Xu Yang

And on this basis, a novel attribute transfer method, named semantic directional decomposition network (SDD-Net), is proposed to achieve semantic-level facial attribute transfer by latent semantic direction decomposition, improving the interpretability and editability of our method.

Image Manipulation Transfer Learning

AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization

no code implementations26 Sep 2021 Qingsong Zhang, Bin Gu, Cheng Deng, Songxiang Gu, Liefeng Bo, Jian Pei, Heng Huang

To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.

Federated Learning Privacy Preserving

Group Contrastive Self-Supervised Learning on Graphs

no code implementations20 Jul 2021 Xinyi Xu, Cheng Deng, Yaochen Xie, Shuiwang Ji

Our framework embeds the given graph into multiple subspaces, of which each representation is prompted to encode specific characteristics of graphs.

Contrastive Learning Self-Supervised Learning

Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval

1 code implementation22 Jun 2021 Zhipeng Wang, Hao Wang, Jiexi Yan, Aming Wu, Cheng Deng

Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches.

Cross-Modal Retrieval Retrieval +1

SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

1 code implementation CVPR 2021 Xu Yang, Cheng Deng, Zhiyuan Dang, Kun Wei, Junchi Yan

Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity.

Representation Learning

Unsupervised Hyperbolic Metric Learning

no code implementations CVPR 2021 Jiexi Yan, Lei Luo, Cheng Deng, Heng Huang

Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning.

Anatomy Metric Learning

Nearest Neighbor Matching for Deep Clustering

1 code implementation CVPR 2021 Zhiyuan Dang, Cheng Deng, Xu Yang, Kun Wei, Heng Huang

Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features.

Deep Clustering

Doubly Contrastive Deep Clustering

1 code implementation9 Mar 2021 Zhiyuan Dang, Cheng Deng, Xu Yang, Heng Huang

In this paper, we present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views to obtain more discriminative features and competitive results.

Contrastive Learning Data Augmentation +1

Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating

no code implementations1 Mar 2021 Qingsong Zhang, Bin Gu, Cheng Deng, Heng Huang

Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage.

Federated Learning

Adversarial Attack on Deep Cross-Modal Hamming Retrieval

no code implementations ICCV 2021 Chao Li, Shangqian Gao, Cheng Deng, Wei Liu, Heng Huang

Specifically, given a target model, we first construct its substitute model to exploit cross-modal correlations within hamming space, with which we create adversarial examples by limitedly querying from a target model.

Adversarial Attack Cross-Modal Retrieval +2

Incremental Embedding Learning via Zero-Shot Translation

1 code implementation31 Dec 2020 Kun Wei, Cheng Deng, Xu Yang, Maosen Li

Different from traditional incremental classification networks, the semantic gap between the embedding spaces of two adjacent tasks is the main challenge for embedding networks under incremental learning setting.

Face Recognition Image Retrieval +4

Adversarial Learning for Robust Deep Clustering

1 code implementation NeurIPS 2020 Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu

Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.

Adversarial Attack Deep Clustering

Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies

1 code implementation NeurIPS 2020 Yuehua Zhu, Muli Yang, Cheng Deng, Wei Liu

In this paper, we propose a novel Proxy-based deep Graph Metric Learning (ProxyGML) approach from the perspective of graph classification, which uses fewer proxies yet achieves better comprehensive performance.

General Classification Graph Classification +1

Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning

no code implementations14 Aug 2020 Bin Gu, An Xu, Zhouyuan Huo, Cheng Deng, Heng Huang

To the best of our knowledge, AFSGD-VP and its SVRG and SAGA variants are the first asynchronous federated learning algorithms for vertically partitioned data.

Federated Learning Privacy Preserving

Projection & Probability-Driven Black-Box Attack

1 code implementation CVPR 2020 Jie Li, Rongrong Ji, Hong Liu, Jianzhuang Liu, Bineng Zhong, Cheng Deng, Qi Tian

For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.

Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation

1 code implementation CVPR 2020 Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, Rongrong Ji

In addition, we address a key challenge in this multi-task setup, i. e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS).

Referring Expression Referring Expression Comprehension +1

Cross-Modal Learning with Adversarial Samples

1 code implementation NeurIPS 2019 Chao Li, Shangqian Gao, Cheng Deng, De Xie, Wei Liu

Extensive experiments on two cross-modal benchmark datasets show that the adversarial examples produced by our CMLA are efficient in fooling a target deep cross-modal hashing network.


DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

no code implementations CVPR 2019 Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, DaCheng Tao

To address this issue, we propose a novel deep unsupervised hashing model, dubbed DistillHash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals.

Semantic Similarity Semantic Textual Similarity

Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval

no code implementations7 May 2019 Xinxun Xu, Hao Wang, Leida Li, Cheng Deng

Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario.

Cross-Modal Retrieval Retrieval +1

Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search

no code implementations18 Apr 2019 Xianglong Liu, Lei Huang, Cheng Deng, Bo Lang, DaCheng Tao

For each hash table, a query-adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search.

Image Retrieval Quantization +1

Shared Predictive Cross-Modal Deep Quantization

no code implementations16 Apr 2019 Erkun Yang, Cheng Deng, Chao Li, Wei Liu, Jie Li, DaCheng Tao

In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search.


Triplet-Based Deep Hashing Network for Cross-Modal Retrieval

no code implementations4 Apr 2019 Cheng Deng, Zhaojia Chen, Xianglong Liu, Xinbo Gao, DaCheng Tao

Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention.

Cross-Modal Retrieval Retrieval +2

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

no code implementations4 Apr 2019 Cheng Deng, Zhao Li, Xinbo Gao, DaCheng Tao

In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge.

General Classification

Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification

no code implementations4 Apr 2019 Cheng Deng, Yumeng Xue, Xianglong Liu, Chao Li, DaCheng Tao

The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel active learning strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross-dataset and intra-image; 3) the learned deep joint spectral-spatial feature representation is more generic and robust than many joint spectral-spatial feature representation.

Active Learning General Classification +2

Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval

no code implementations3 Apr 2019 Hao Wang, Cheng Deng, Xinxu Xu, Wei Liu, Xinbo Gao, DaCheng Tao

Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image.

Retrieval Sketch-Based Image Retrieval +1

Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

no code implementations6 Mar 2019 Chao Li, Cheng Deng, Lei Wang, De Xie, Xianglong Liu

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval.

Cross-Modal Retrieval Retrieval

Fully-Featured Attribute Transfer

no code implementations17 Feb 2019 De Xie, Muli Yang, Cheng Deng, Wei Liu, DaCheng Tao

Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years.

Image Generation

Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

1 code implementation23 Jan 2019 Shaohui Lin, Rongrong Ji, Yuchao Li, Cheng Deng, Xuelong. Li

In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speedup the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries.

Domain Adaptation object-detection +2

Towards Visual Feature Translation

1 code implementation CVPR 2019 Jie Hu, Rongrong Ji, Hong Liu, Shengchuan Zhang, Cheng Deng, Qi Tian

In this paper, we make the first attempt towards visual feature translation to break through the barrier of using features across different visual search systems.


Bilevel Distance Metric Learning for Robust Image Recognition

no code implementations NeurIPS 2018 Jie Xu, Lei Luo, Cheng Deng, Heng Huang

Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems.

Metric Learning

Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training

1 code implementation CVPR 2019 Feng Zheng, Cheng Deng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, Feiyue Huang, Rongrong Ji

Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other.

Person Re-Identification

Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines

no code implementations ICML 2018 Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang

Asynchronous parallel stochastic gradient optimization has been playing a pivotal role to solve large-scale machine learning problems in big data applications.

Ensemble Learning

Direct Shape Regression Networks for End-to-End Face Alignment

no code implementations CVPR 2018 Xin Miao, Xian-Tong Zhen, Xianglong Liu, Cheng Deng, Vassilis Athitsos, Heng Huang

In this paper, we propose the direct shape regression network (DSRN) for end-to-end face alignment by jointly handling the aforementioned challenges in a unified framework.

Face Alignment regression +1

CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation

no code implementations28 May 2018 Xiao Liu, Shengchuan Zhang, Hong Liu, Xin Liu, Cheng Deng, Rongrong Ji

In principle, CerfGAN contains a novel component, i. e., a multi-class discriminator (MCD), which gives the model an extremely powerful ability to match multiple translation mappings.

Face Hallucination Image-to-Image Translation +2

Semantic Structure-based Unsupervised Deep Hashing

1 code implementation IJCAI2018 2018 Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, DaCheng Tao

Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency.

Semantic Similarity Semantic Textual Similarity

Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

1 code implementation CVPR 2018 Chao Li, Cheng Deng, Ning li, Wei Liu, Xinbo Gao, DaCheng Tao

In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations.

Cross-Modal Retrieval Retrieval

Learning A Structured Optimal Bipartite Graph for Co-Clustering

no code implementations NeurIPS 2017 Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang

In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.

Group Sparse Additive Machine

no code implementations NeurIPS 2017 Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

Additive models Classification +2

Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

1 code implementation ICCV 2017 Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, Heng Huang

We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments.

Deep Clustering Image Clustering

Multilinear Hyperplane Hashing

no code implementations CVPR 2016 Xianglong Liu, Xinjie Fan, Cheng Deng, Zhujin Li, Hao Su, DaCheng Tao

Despite its successful progress in classic point-to-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up in many applications like active learning with SVMs.

Active Learning Quantization

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

no code implementations5 Sep 2015 Wenhao Jiang, Cheng Deng, Wei Liu, Feiping Nie, Fu-Lai Chung, Heng Huang

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.

Domain Adaptation

Collaborative Hashing

no code implementations CVPR 2014 Xianglong Liu, Junfeng He, Cheng Deng, Bo Lang

Hashing technique has become a promising approach for fast similarity search.

Image Retrieval

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