Search Results for author: Qingshan Liu

Found 49 papers, 16 papers with code

ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices

no code implementations ECCV 2020 Xiangyu He, Zitao Mo, Ke Cheng, Weixiang Xu, Qinghao Hu, Peisong Wang, Qingshan Liu, Jian Cheng

The matrix composed of basis vectors is referred to as the proxy matrix, and auxiliary variables serve as the coefficients of this linear combination.

Binarization Quantization

Learning Memory Augmented Cascading Network for Compressed Sensing of Images

1 code implementation ECCV 2020 Jiwei Chen, Yubao Sun, Qingshan Liu, Rui Huang

The IDR module is designed to reconstruct the remaining details from the residual measurement vector, and MRU is employed to update the residual measurement vector and feed it into the next IDR module.

Cooperative Sentiment Agents for Multimodal Sentiment Analysis

1 code implementation19 Apr 2024 Shanmin Wang, Hui Shuai, Qingshan Liu, Fei Wang

In this paper, we propose a new Multimodal Representation Learning (MRL) method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive interaction between modalities through Cooperative Sentiment Agents, named Co-SA.

Disentanglement Multimodal Emotion Recognition +1

Defenses in Adversarial Machine Learning: A Survey

no code implementations13 Dec 2023 Baoyuan Wu, Shaokui Wei, Mingli Zhu, Meixi Zheng, Zihao Zhu, Mingda Zhang, Hongrui Chen, Danni Yuan, Li Liu, Qingshan Liu

Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases.

Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection

1 code implementation CVPR 2024 Zhiyuan Yan, Yuhao Luo, Siwei Lyu, Qingshan Liu, Baoyuan Wu

Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data.

DeepFake Detection Face Swapping +1

Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-based Video Incremental Learning

1 code implementation1 Jun 2023 Shengqin Jiang, Yaoyu Fang, Haokui Zhang, Qingshan Liu, Yuankai Qi, Yang Yang, Peng Wang

Rehearsal-based video incremental learning often employs knowledge distillation to mitigate catastrophic forgetting of previously learned data.

Incremental Learning Knowledge Distillation +1

Crowd Counting with Online Knowledge Learning

no code implementations18 Mar 2023 Shengqin Jiang, Bowen Li, Fengna Cheng, Qingshan Liu

Moreover, we propose a feature relation distillation method which allows the student branch to more effectively comprehend the evolution of inter-layer features by constructing a new inter-layer relationship matrix.

Crowd Counting Edge-computing +1

Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective

1 code implementation19 Feb 2023 Baoyuan Wu, Zihao Zhu, Li Liu, Qingshan Liu, Zhaofeng He, Siwei Lyu

Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans.

Backdoor Attack

Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning

no code implementations ICCV 2023 Tiankang Su, Huihui Song, Dong Liu, Bo Liu, Qingshan Liu

We integrate our offline training and online fine-tuning in a unified framework for unsupervised video object segmentation and dub our method Online Adversarial Self-Tuning (OAST).

Object Pseudo Label +4

A Unified Object Counting Network with Object Occupation Prior

1 code implementation29 Dec 2022 Shengqin Jiang, Qing Wang, Fengna Cheng, Yuankai Qi, Qingshan Liu

In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task.

Crowd Counting Knowledge Distillation +2

PointAttN: You Only Need Attention for Point Cloud Completion

1 code implementation16 Mar 2022 Jun Wang, Ying Cui, Dongyan Guo, Junxia Li, Qingshan Liu, Chunhua Shen

To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for processing point cloud in a per-point manner to eliminate kNNs.

Decoder Point Cloud Completion

DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance

no code implementations CVPR 2021 Kaihua Zhang, Mingliang Dong, Bo Liu, Xiao-Tong Yuan, Qingshan Liu

This dense correlation volumes enables the network to accurately discover the structured pair-wise pixel similarities among the common salient objects.

Saliency Detection

Robust Lightweight Facial Expression Recognition Network with Label Distribution Training

1 code implementation AAAI Conference on Artificial Intelligence 2021 Zengqun Zhao, Qingshan Liu, Feng Zhou

This paper presents an efficiently robust facial expression recognition (FER) network, named EfficientFace, which holds much fewer parameters but more robust to the FER in the wild.

Facial Expression Recognition (FER)

Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction

no code implementations18 Dec 2020 Yubao Sun, Ying Yang, Qingshan Liu, Mohan Kankanhalli

Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period.

Compressive Sensing

Meta-Learning with Network Pruning

no code implementations ECCV 2020 Hongduan Tian, Bo Liu, Xiao-Tong Yuan, Qingshan Liu

To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network.

Few-Shot Learning Network Pruning

Hyperspectral Image Classification with Attention Aided CNNs

1 code implementation25 May 2020 Renlong Hang, Zhu Li, Qingshan Liu, Pedram Ghamisi, Shuvra S. Bhattacharyya

Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.

Classification General Classification +1

Dual Temporal Memory Network for Efficient Video Object Segmentation

no code implementations13 Mar 2020 Kaihua Zhang, Long Wang, Dong Liu, Bo Liu, Qingshan Liu, Zhu Li

We present an end-to-end network which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VOS.

Object One-shot visual object segmentation +4

Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

1 code implementation CVPR 2020 Kaihua Zhang, Tengpeng Li, Shiwen Shen, Bo Liu, Jin Chen, Qingshan Liu

Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion.

Clustering Co-Salient Object Detection +2

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

no code implementations4 Feb 2020 Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, Qingshan Liu

For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy.

Classification General Classification

Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network

1 code implementation2 Jan 2020 Jin Chen, Huihui Song, Kaihua Zhang, Bo Liu, Qingshan Liu

Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP).

Saliency Prediction Video Saliency Detection +1

Deep Object Co-segmentation via Spatial-Semantic Network Modulation

1 code implementation29 Nov 2019 Kaihua Zhang, Jin Chen, Bo Liu, Qingshan Liu

With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image.

General Classification Image Classification +2

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

no code implementations28 Feb 2019 Renlong Hang, Qingshan Liu, Danfeng Hong, Pedram Ghamisi

The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands.

Classification General Classification +1

Hyperspectral image classification using spectral-spatial LSTMs

no code implementations20 Aug 2018 Feng Zhou, Renlong Hang, Qingshan Liu, Xiaotong Yuan

Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature.

Classification General Classification +1

Robust Subspace Clustering with Compressed Data

no code implementations30 Mar 2018 Guangcan Liu, Zhao Zhang, Qingshan Liu, Kongkai Xiong

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e. g., clustering) of compressed data.

Clustering Computational Efficiency +1

A New Theory for Matrix Completion

no code implementations NeurIPS 2017 Guangcan Liu, Qingshan Liu, Xiaotong Yuan

To break through the limits of random sampling, this paper introduces a new hypothesis called \emph{isomeric condition}, which is provably weaker than the assumption of uniform sampling and arguably holds even when the missing data is placed irregularly.

Matrix Completion

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

1 code implementation23 Mar 2017 Qingshan Liu, Feng Zhou, Renlong Hang, Xiao-Tong Yuan

In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it.

General Classification Hyperspectral Image Classification

Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning

no code implementations24 Dec 2016 Kaihua Zhang, Xuejun Li, Qingshan Liu

Then, with the updated appearances, we formulate a spatio-temporal graphical model comprised of the superpixel label consistency potentials.

Segmentation Semantic Segmentation +4

Learning Additive Exponential Family Graphical Models via \ell_{2,1}-norm Regularized M-Estimation

no code implementations NeurIPS 2016 Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu

We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms.

Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

no code implementations11 Nov 2016 Qingshan Liu, Renlong Hang, Huihui Song, Fuping Zhu, Javier Plaza, Antonio Plaza

In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification.

Classification General Classification +3

Visual Tracking via Boolean Map Representations

no code implementations30 Oct 2016 Kaihua Zhang, Qingshan Liu, Ming-Hsuan Yang

In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking.

Visual Tracking

Self-Paced Multi-Task Learning

no code implementations6 Apr 2016 Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, Hongyuan Zha

In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL).

Multi-Task Learning

A Self-Paced Regularization Framework for Multi-Label Learning

no code implementations22 Mar 2016 Changsheng Li, Fan Wei, Junchi Yan, Weishan Dong, Qingshan Liu, Xiao-Yu Zhang, Hongyuan Zha

In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime.

Multi-Label Learning

Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification

no code implementations3 Mar 2016 Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu, DaCheng Tao

In the second step, hypergraph is used to represent the high order relationships between each datum and its prominent samples by regarding them as a hyperedge.

Clustering General Classification +3

Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval

no code implementations22 Feb 2016 Yubao Sun, Renlong Hang, Qingshan Liu, Fuping Zhu, Hucheng Pei

In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval.

regression Retrieval

Additive Nearest Neighbor Feature Maps

no code implementations ICCV 2015 Zhenzhen Wang, Xiao-Tong Yuan, Qingshan Liu, Shuicheng Yan

In this paper, we present a concise framework to approximately construct feature maps for nonlinear additive kernels such as the Intersection, Hellinger's, and Chi^2 kernels.

Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

no code implementations21 Apr 2015 Qingshan Liu, Jing Yang, Kaihua Zhang, Yi Wu

Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.

feature selection

Joint Active Learning with Feature Selection via CUR Matrix Decomposition

no code implementations4 Mar 2015 Changsheng Li, Xiangfeng Wang, Weishan Dong, Junchi Yan, Qingshan Liu, Hongyuan Zha

In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling.

Active Learning feature selection

Robust Visual Tracking via Convolutional Networks

no code implementations19 Jan 2015 Kaihua Zhang, Qingshan Liu, Yi Wu, Ming-Hsuan Yang

In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.

Visual Tracking

Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data

no code implementations18 Dec 2014 Changsheng Li, Fan Wei, Weishan Dong, Qingshan Liu, Xiangfeng Wang, Xin Zhang

MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement.


Newton Greedy Pursuit: A Quadratic Approximation Method for Sparsity-Constrained Optimization

no code implementations CVPR 2014 Xiao-Tong Yuan, Qingshan Liu

The main theme of this type of methods is to evaluate the function gradient in the previous iteration to update the non-zero entries and their values in the next iteration.

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