Search Results for author: Nanning Zheng

Found 86 papers, 27 papers with code

Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models

no code implementations7 Mar 2022 Shengnan An, Yifei Li, Zeqi Lin, Qian Liu, Bei Chen, Qiang Fu, Weizhu Chen, Nanning Zheng, Jian-Guang Lou

This motivates us to propose input-tuning, which fine-tunes both the continuous prompts and the input representations, leading to a more effective way to adapt unfamiliar inputs to frozen PLMs.

Language Modelling Natural Language Understanding +1

Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding

no code implementations3 Feb 2022 Pu Zhang, Lei Bai, Jianru Xue, Jianwu Fang, Nanning Zheng, Wanli Ouyang

Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories.

Object Detection Trajectory Forecasting

TransVPR: Transformer-based place recognition with multi-level attention aggregation

no code implementations6 Jan 2022 Ruotong Wang, Yanqing Shen, Weiliang Zuo, Sanping Zhou, Nanning Zheng

In addition, the output tokens from Transformer layers filtered by the fused attention mask are considered as key-patch descriptors, which are used to perform spatial matching to re-rank the candidates retrieved by the global image features.

Autonomous Driving Visual Place Recognition

Co-evolution Transformer for Protein Contact Prediction

1 code implementation NeurIPS 2021 He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu

These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reflection of collective covariation patterns of numerous homologous proteins.

Dynamic Grained Encoder for Vision Transformers

1 code implementation NeurIPS 2021 Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng

Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.

Image Classification Language Modelling +1

LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering

1 code implementation29 Nov 2021 Jingjing Jiang, Ziyi Liu, Nanning Zheng

Video Question Answering (VideoQA), aiming to correctly answer the given question based on understanding multi-modal video content, is challenging due to the rich video content.

Question Answering Video Question Answering +3

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

no code implementations14 Oct 2021 Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.

Drug Discovery Pose Prediction

Foreground-attention in neural decoding: Guiding Loop-Enc-Dec to reconstruct visual stimulus images from fMRI

no code implementations29 Sep 2021 Kai Chen, Yongqiang Ma, Mingyang Sheng, Nanning Zheng

Inspired by the mechanism of human visual attention, in this paper, we propose a novel method of reconstructing visual stimulus images, which first decodes the distribution of visual attention from fMRI, and then reconstructs the visual images guided by visual attention.

Image Reconstruction

Model-based Decision Making with Imagination for Autonomous Parking

1 code implementation25 Aug 2021 Ziyue Feng, Yu Chen, Shitao Chen, Nanning Zheng

The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks.

Autonomous Driving Decision Making

Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction

1 code implementation ICCV 2021 Fang Zheng, Le Wang, Sanping Zhou, Wei Tang, Zhenxing Niu, Nanning Zheng, Gang Hua

Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area.

Graph Attention Trajectory Prediction

X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering

1 code implementation24 Jul 2021 Jingjing Jiang, Ziyi Liu, Yifan Liu, Zhixiong Nan, Nanning Zheng

In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to implicitly model the problem.

Out-of-Distribution Generalization Question Answering +2

Video Imprint

no code implementations7 Jun 2021 Zhanning Gao, Le Wang, Nebojsa Jojic, Zhenxing Niu, Nanning Zheng, Gang Hua

In the proposed framework, a dedicated feature alignment module is incorporated for redundancy removal across frames to produce the tensor representation, i. e., the video imprint.

Frame Language Modelling

Adversarial Attack and Defense in Deep Ranking

1 code implementation7 Jun 2021 Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Nanning Zheng, Gang Hua

In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.

Adversarial Attack Adversarial Robustness

Practical Relative Order Attack in Deep Ranking

2 code implementations ICCV 2021 Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Yinghui Xu, Nanning Zheng, Gang Hua

In this paper, we formulate a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order among a selected set of candidates according to an attacker-specified permutation, with limited interference to other unrelated candidates.

Adversarial Attack

A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN

no code implementations1 Mar 2021 He Zhang, Zhixiong Nan, Tao Yang, Yifan Liu, Nanning Zheng

In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision.

Autonomous Driving

Towards Building A Group-based Unsupervised Representation Disentanglement Framework

1 code implementation ICLR 2022 Tao Yang, Xuanchi Ren, Yuwang Wang, Wenjun Zeng, Nanning Zheng

We then propose a model, based on existing VAE-based methods, to tackle the unsupervised learning problem of the framework.

Disentanglement

Practical Order Attack in Deep Ranking

no code implementations1 Jan 2021 Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Xu Yinghui, Nanning Zheng, Gang Hua

The objective of this paper is to formalize and practically implement a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order of a selected set of candidates according to a permutation vector predefined by the attacker, with only limited interference to other unrelated candidates.

Adversarial Attack Image Retrieval

Fine-Grained Dynamic Head for Object Detection

1 code implementation NeurIPS 2020 Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.

Object Detection

End-to-End Object Detection with Fully Convolutional Network

1 code implementation CVPR 2021 JianFeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

Mainstream object detectors based on the fully convolutional network has achieved impressive performance.

Object Detection

Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation

no code implementations7 Dec 2020 Lipeng Wan, Xuwei Song, Xuguang Lan, Nanning Zheng

General methods for policy based multi-agent reinforcement learning to solve the challenge introduce differentiate value functions or advantage functions for individual agents.

Multi-agent Reinforcement Learning Starcraft

Learning to Infer Unseen Attribute-Object Compositions

no code implementations27 Oct 2020 Hui Chen, Zhixiong Nan, Jingjing Jiang, Nanning Zheng

The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people.

Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression

no code implementations8 Sep 2020 Jianji Wang, Qi Liu, Shupei Zhang, Nanning Zheng, Fei-Yue Wang

By the proposed method, the computational complexity is reduced from $O(\frac{1}{6}{k^3}+mk^2+mkd)$ to $O(\frac{1}{6}{k^3}+\frac{1}{2}mk^2)$ for each candidate subset in sparse regression.

A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning

2 code implementations ECCV 2020 Xingyu Chen, Xuguang Lan, Fuchun Sun, Nanning Zheng

Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.

Generalized Zero-Shot Learning

REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds

1 code implementation28 Feb 2020 Binglei Zhao, Hanbo Zhang, Xuguang Lan, Haoyu Wang, Zhiqiang Tian, Nanning Zheng

Reliable robotic grasping in unstructured environments is a crucial but challenging task.

Robotics

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

1 code implementation4 Jan 2020 Zirui Zhao, Yijun Mao, Yan Ding, Pengju Ren, Nanning Zheng

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction.

Autonomous Driving Semantic Segmentation +1

BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS

no code implementations ICLR 2020 Hui Chen, Zhixiong Nan, Nanning Zheng

This paper handles a challenging problem, unseen attribute-object pair recognition, which asks a model to simultaneously recognize the attribute type and the object type of a given image while this attribute-object pair is not included in the training set.

EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks

no code implementations3 Sep 2019 Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng

For an RNN block, an EleAttG is used for adaptively modulating the input by assigning different levels of importance, i. e., attention, to each element/dimension of the input.

Action Recognition Skeleton Based Action Recognition

An encoding framework with brain inner state for natural image identification

no code implementations22 Aug 2019 Hao Wu, Ziyu Zhu, Jiayi Wang, Nanning Zheng, Badong Chen

The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain.

Brain Decoding

Hindsight Trust Region Policy Optimization

1 code implementation29 Jul 2019 Hanbo Zhang, Site Bai, Xuguang Lan, David Hsu, Nanning Zheng

We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards.

Atari Games Policy Gradient Methods

SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction

1 code implementation CVPR 2019 Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, Nanning Zheng

In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism.

Pedestrian Trajectory Prediction Trajectory Prediction

Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition

no code implementations23 Oct 2018 Yuanliu Liu, Ang Li, Zejian yuan, Badong Chen, Nanning Zheng

We propose a Consistency-aware Selective Fusion (CSF) to integrate the pairwise orders into a globally consistent order.

Intrinsic Image Decomposition

A Real-time Robotic Grasp Approach with Oriented Anchor Box

no code implementations8 Sep 2018 Hanbo Zhang, Xinwen Zhou, Xuguang Lan, Jin Li, Zhiqiang Tian, Nanning Zheng

The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors.

Robotics

Transductive Semi-Supervised Deep Learning using Min-Max Features

no code implementations ECCV 2018 Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, Nanning Zheng

In this paper, we propose Transductive Semi-Supervised Deep Learning (TSSDL) method that is effective for training Deep Convolutional Neural Network (DCNN) models.

General Classification Image Classification

Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification

no code implementations ECCV 2018 Xing Wei, Yue Zhang, Yihong Gong, Jiawei Zhang, Nanning Zheng

The reason is that the bilinear feature matrix is sensitive to the magnitudes and correlations of local CNN feature elements which can be measured by its singular values.

Fine-Grained Image Classification Fine-Grained Visual Recognition +1

ROI-based Robotic Grasp Detection for Object Overlapping Scenes

no code implementations30 Aug 2018 Hanbo Zhang, Xuguang Lan, Site Bai, Xinwen Zhou, Zhiqiang Tian, Nanning Zheng

Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset.

Robotics

Adding Attentiveness to the Neurons in Recurrent Neural Networks

no code implementations ECCV 2018 Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng

We propose adding a simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block (e. g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability.

Action Recognition Skeleton Based Action Recognition

Discriminative Feature Learning with Foreground Attention for Person Re-Identification

no code implementations4 Jul 2018 Sanping Zhou, Jinjun Wang, Deyu Meng, Yudong Liang, Yihong Gong, Nanning Zheng

Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons.

Multi-Task Learning Person Re-Identification

Kernelized Subspace Pooling for Deep Local Descriptors

no code implementations CVPR 2018 Xing Wei, Yue Zhang, Yihong Gong, Nanning Zheng

Experimental results on several patch matching benchmarks show that our method outperforms the state-of-the-arts significantly.

Patch Matching

View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition

2 code implementations20 Apr 2018 Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng

In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner.

Action Recognition Skeleton Based Action Recognition

Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition

no code implementations19 Mar 2018 Jinliang Zang, Le Wang, Ziyi Liu, Qilin Zhang, Zhenxing Niu, Gang Hua, Nanning Zheng

Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs).

Action Recognition

Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network

no code implementations13 Mar 2018 Kai Yi, Zhiqiang Jian, Shitao Chen, Nanning Zheng

At present, the performance of deep neural network in general object detection is comparable to or even surpasses that of human beings.

Autonomous Driving Decision Making +1

Augmented Space Linear Model

no code implementations1 Feb 2018 Zhengda Qin, Badong Chen, Nanning Zheng, Jose C. Principe

In this paper, we propose a linear model called Augmented Space Linear Model (ASLM), which uses the full joint space of input and desired signal as the projection space and approaches the performance of nonlinear models.

A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections

no code implementations1 Dec 2017 Siyu Yu, Nanning Zheng, Yongqiang Ma, Hao Wu, Badong Chen

Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals.

Brain Decoding

Quantized Minimum Error Entropy Criterion

no code implementations11 Oct 2017 Badong Chen, Lei Xing, Nanning Zheng, Jose C. Príncipe

Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning.

Quantization

Deep Self-Paced Learning for Person Re-Identification

no code implementations7 Oct 2017 Sanping Zhou, Jinjun Wang, Deyu Meng, Xiaomeng Xin, Yubing Li, Yihong Gong, Nanning Zheng

In this paper, we propose a novel deep self-paced learning (DSPL) algorithm to alleviate this problem, in which we apply a self-paced constraint and symmetric regularization to help the relative distance metric training the deep neural network, so as to learn the stable and discriminative features for person Re-ID.

Person Re-Identification

Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification

no code implementations18 Aug 2017 Sanping Zhou, Jinjun Wang, Rui Shi, Qiqi Hou, Yihong Gong, Nanning Zheng

The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN).

Person Re-Identification

Associations among Image Assessments as Cost Functions in Linear Decomposition: MSE, SSIM, and Correlation Coefficient

no code implementations4 Aug 2017 Jianji Wang, Nanning Zheng, Badong Chen, Jose C. Principe

Moreover, for a target vector, the ratio of the corresponding affine parameters in the MSE-based linear decomposition scheme and the SSIM-based scheme is a constant, which is just the value of PCC between the target vector and its estimated vector.

SSIM

Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification

no code implementations25 Jul 2017 De Cheng, Yihong Gong, Zhihui Li, Weiwei Shi, Alexander G. Hauptmann, Nanning Zheng

The proposed method can take full advantages of the structured distance relationships among these training samples, with the constructed complete graph.

Person Re-Identification

Point to Set Similarity Based Deep Feature Learning for Person Re-Identification

no code implementations CVPR 2017 Sanping Zhou, Jinjun Wang, Jiayun Wang, Yihong Gong, Nanning Zheng

One of the key issues for deep learning based person Re-ID is the selection of proper similarity comparison criteria, and the performance of learned features using existing criterion based on pairwise similarity is still limited, because only P2P distances are mostly considered.

Person Re-Identification

Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach

no code implementations1 May 2017 Ziyi Liu, Siyu Yu, Xiao Wang, Nanning Zheng

Experiments show that our unsupervised approach is efficient and robust for detecting drivable area for self-driving cars.

Self-Driving Cars

Single Image Super Resolution - When Model Adaptation Matters

no code implementations31 Mar 2017 Yudong Liang, Radu Timofte, Jinjun Wang, Yihong Gong, Nanning Zheng

The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors.

Image Super-Resolution

View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

1 code implementation ICCV 2017 Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng

Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.

Action Recognition Frame +1

Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network

no code implementations23 Mar 2017 Yudong Liang, Ze Yang, Kai Zhang, Yihui He, Jinjun Wang, Nanning Zheng

To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed.

Image Super-Resolution SSIM

Robust Learning with Kernel Mean p-Power Error Loss

no code implementations21 Dec 2016 Badong Chen, Lei Xing, Xin Wang, Jing Qin, Nanning Zheng

Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing.

Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering

no code implementations1 Aug 2016 Badong Chen, Lei Xing, Bin Xu, Haiquan Zhao, Nanning Zheng, Jose C. Principe

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning.

Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function

no code implementations CVPR 2016 De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, Nanning Zheng

Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras.

Person Re-Identification

Similarity Learning With Spatial Constraints for Person Re-Identification

no code implementations CVPR 2016 Dapeng Chen, Zejian yuan, Badong Chen, Nanning Zheng

We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion.

Person Re-Identification

Counting Grid Aggregation for Event Retrieval and Recognition

no code implementations5 Apr 2016 Zhanning Gao, Gang Hua, Dongqing Zhang, Jianru Xue, Nanning Zheng

Event retrieval and recognition in a large corpus of videos necessitates a holistic fixed-size visual representation at the video clip level that is comprehensive, compact, and yet discriminative.

Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing

no code implementations4 Feb 2016 Fei Zhu, Abderrahim Halimi, Paul Honeine, Badong Chen, Nanning Zheng

In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.

Hyperspectral Unmixing

Contour Guided Hierarchical Model for Shape Matching

no code implementations ICCV 2015 Yuanqi Su, Yuehu Liu, Bonan Cuan, Nanning Zheng

For the purpose, we divide the shape template into overlapped parts and model the matching through a part-based layered structure that uses the latent variable to constrain parts' deformation.

Illumination Robust Color Naming via Label Propagation

no code implementations ICCV 2015 Yuanliu liu, Zejian yuan, Badong Chen, Jianru Xue, Nanning Zheng

In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically.

Image Retrieval

Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification

no code implementations CVPR 2015 Dapeng Chen, Zejian yuan, Gang Hua, Nanning Zheng, Jingdong Wang

We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person.

Learning-To-Rank Patch Matching +1

Saturation-Preserving Specular Reflection Separation

no code implementations CVPR 2015 Yuanliu Liu, Zejian yuan, Nanning Zheng, Yang Wu

Specular reflection generally decreases the saturation of surface colors, which will be possibly confused with other colors that have the same hue but lower saturation.

Generalized Correntropy for Robust Adaptive Filtering

no code implementations12 Apr 2015 Badong Chen, Lei Xing, Haiquan Zhao, Nanning Zheng, José C. Príncipe

In this work, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel (not necessarily a Mercer kernel), and present some important properties.

Salient Object Detection: A Discriminative Regional Feature Integration Approach

no code implementations CVPR 2013 Huaizu Jiang, Zejian yuan, Ming-Ming Cheng, Yihong Gong, Nanning Zheng, Jingdong Wang

Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.

RGB Salient Object Detection Salient Object Detection +1

Kernel Least Mean Square with Adaptive Kernel Size

no code implementations23 Jan 2014 Badong Chen, Junli Liang, Nanning Zheng, Jose C. Principe

Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS).

Time Series Time Series Prediction

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