Search Results for author: Zhanyu Ma

Found 82 papers, 38 papers with code

Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing

no code implementations22 Oct 2024 Runpu Wei, Zijin Yin, Kongming Liang, Min Min, Chengwei Pan, Gang Yu, Haonan Huang, Yan Liu, Zhanyu Ma

To benchmark the model robustness, we focus on evaluating the robustness of segmentation models on the polyps with various attributes (e. g. location and size) and healthy samples.

Attribute Benchmarking +3

I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow

no code implementations10 Oct 2024 Ruoyi Du, Dongyang Liu, Le Zhuo, Qin Qi, Hongsheng Li, Zhanyu Ma, Peng Gao

Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models.

2k

Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network

1 code implementation29 Jul 2024 Wenjie Li, Heng Guo, Xuannan Liu, Kongming Liang, Jiani Hu, Zhanyu Ma, Jun Guo

Previous methods typically employ an encoder-decoder structure to extract facial structural features, where the direct downsampling inevitably introduces distortions, especially to high-frequency features such as edges.

Decoder Super-Resolution

Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT

1 code implementation5 Jun 2024 Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongjie Huang, Wenze Liu, Lirui Zhao, Fu-Yun Wang, Zhanyu Ma, Xu Luo, Zehan Wang, Kaipeng Zhang, Xiangyang Zhu, Si Liu, Xiangyu Yue, Dingning Liu, Wanli Ouyang, Ziwei Liu, Yu Qiao, Hongsheng Li, Peng Gao

Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.

Point Cloud Generation Text-to-Image Generation

Benchmarking Segmentation Models with Mask-Preserved Attribute Editing

1 code implementation CVPR 2024 Zijin Yin, Kongming Liang, Bing Li, Zhanyu Ma, Jun Guo

We evaluate a broad variety of semantic segmentation models, spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations.

Attribute Benchmarking +2

Vision-language Assisted Attribute Learning

no code implementations12 Dec 2023 Kongming Liang, Xinran Wang, Rui Wang, Donghui Gao, Ling Jin, Weidong Liu, Xiatian Zhu, Zhanyu Ma, Jun Guo

Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization.

Attribute Language Modelling +2

HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors

1 code implementation26 Nov 2023 Junhui Yin, Wei Yin, Hao Chen, Xuqian Ren, Zhanyu Ma, Jun Guo, Yifan Liu

These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays.

Novel View Synthesis

DemoFusion: Democratising High-Resolution Image Generation With No $$$

1 code implementation CVPR 2024 Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma

High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls.

Image Generation

Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition

1 code implementation18 Sep 2023 Ming-Zhe Li, Zhen Jia, Zhang Zhang, Zhanyu Ma, Liang Wang

In order to solve this dilemma, we propose a multi-semantic fusion (MSF) model for improving the performance of GZSSAR, where two kinds of class-level textual descriptions (i. e., action descriptions and motion descriptions), are collected as auxiliary semantic information to enhance the learning efficacy of generalizable skeleton features.

Action Recognition cross-modal alignment +2

Super-Resolution Information Enhancement For Crowd Counting

1 code implementation13 Mar 2023 Jiahao Xie, Wei Xu, Dingkang Liang, Zhanyu Ma, Kongming Liang, Weidong Liu, Rui Wang, Ling Jin

As the proposed method requires SR labels, we further propose a Super-Resolution Crowd Counting dataset (SR-Crowd).

Crowd Counting Super-Resolution

An Erudite Fine-Grained Visual Classification Model

no code implementations CVPR 2023 Dongliang Chang, Yujun Tong, Ruoyi Du, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma

Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets.

Classification Disentanglement +2

Multi-View Active Fine-Grained Visual Recognition

1 code implementation ICCV 2023 Ruoyi Du, Wenqing Yu, Heqing Wang, Ting-En Lin, Dongliang Chang, Zhanyu Ma

Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2 images.

Fine-Grained Image Classification Fine-Grained Visual Recognition

Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

1 code implementation30 Nov 2022 Jijie Wu, Dongliang Chang, Aneeshan Sain, Xiaoxu Li, Zhanyu Ma, Jie Cao, Jun Guo, Yi-Zhe Song

Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).

Few-Shot Image Classification Few-Shot Learning +2

ScaleNet: Searching for the Model to Scale

1 code implementation15 Jul 2022 Jiyang Xie, Xiu Su, Shan You, Zhanyu Ma, Fei Wang, Chen Qian

Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.

Multi-View Active Fine-Grained Recognition

1 code implementation2 Jun 2022 Ruoyi Du, Wenqing Yu, Heqing Wang, Dongliang Chang, Ting-En Lin, Yongbin Li, Zhanyu Ma

As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences.

Fine-Grained Image Classification

Learning Invariant Visual Representations for Compositional Zero-Shot Learning

1 code implementation1 Jun 2022 Tian Zhang, Kongming Liang, Ruoyi Du, Xian Sun, Zhanyu Ma, Jun Guo

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set.

Attribute Compositional Zero-Shot Learning +2

Caption Feature Space Regularization for Audio Captioning

1 code implementation18 Apr 2022 Yiming Zhang, Hong Yu, Ruoyi Du, Zhanyu Ma, Yuan Dong

To eliminate this negative effect, in this paper, we propose a two-stage framework for audio captioning: (i) in the first stage, via the contrastive learning, we construct a proxy feature space to reduce the distances between captions correlated to the same audio, and (ii) in the second stage, the proxy feature space is utilized as additional supervision to encourage the model to be optimized in the direction that benefits all the correlated captions.

Audio captioning Contrastive Learning +1

Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction

no code implementations20 Jan 2022 Jingye Wang, Ruoyi Du, Dongliang Chang, Kongming Liang, Zhanyu Ma

Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i. i. d.

Data Augmentation Decoder +3

Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data

1 code implementation6 Dec 2021 Ruoyi Du, Dongliang Chang, Zhanyu Ma, Yi-Zhe Song, Jun Guo

Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for.

Fine-Grained Image Classification

Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis

1 code implementation ACL 2021 Ruifan Li, Hao Chen, Fangxiang Feng, Zhanyu Ma, Xiaojie Wang, Eduard Hovy

To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification

no code implementations21 Jun 2021 Chenyu Guo, Jiyang Xie, Kongming Liang, Xian Sun, Zhanyu Ma

Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC.

Fine-Grained Image Classification

CMF: Cascaded Multi-model Fusion for Referring Image Segmentation

1 code implementation16 Jun 2021 Jianhua Yang, Yan Huang, Zhanyu Ma, Liang Wang

To solve this problem, we propose a simple yet effective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple atrous convolutional layers in parallel and further introduces a cascaded branch to fuse visual and linguistic features.

Image Segmentation Segmentation +1

Structured DropConnect for Uncertainty Inference in Image Classification

1 code implementation16 Jun 2021 Wenqing Zheng, Jiyang Xie, Weidong Liu, Zhanyu Ma

For image classification tasks, we propose a structured DropConnect (SDC) framework to model the output of a deep neural network by a Dirichlet distribution.

Classification Image Classification +1

Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification

no code implementations7 Jun 2021 Yifeng Ding, Shuwei Dong, Yujun Tong, Zhanyu Ma, Bo Xiao, Haibin Ling

Classifying the sub-categories of an object from the same super-category (e. g., bird) in a fine-grained visual classification (FGVC) task highly relies on mining multiple discriminative features.

Fine-Grained Image Classification

Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments

no code implementations17 May 2021 Xiaoxu Li, Xiaochen Yang, Zhanyu Ma, Jing-Hao Xue

Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images.

Classification Few-Shot Image Classification +3

Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning

no code implementations1 Apr 2021 Junhui Yin, Jiayan Qiu, Siqing Zhang, Jiyang Xie, Zhanyu Ma, Jun Guo

Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models.

Clustering Unsupervised Person Re-Identification

DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification

no code implementations1 Apr 2021 Junhui Yin, Zhanyu Ma, Jiyang Xie, Shibo Nie, Kongming Liang, Jun Guo

Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching.

Cross-Modality Person Re-identification Person Re-Identification

Duplex Contextual Relation Network for Polyp Segmentation

1 code implementation11 Mar 2021 Zijin Yin, Kongming Liang, Zhanyu Ma, Jun Guo

However, previous methods only focus on learning the dependencies between the position within an individual image and ignore the contextual relation across different images.

Position Relation +1

Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features

2 code implementations31 Jan 2021 Dongliang Chang, Yixiao Zheng, Zhanyu Ma, Ruoyi Du, Kongming Liang

Finally, we can obtain multiple discriminative regions on high-level feature channels and obtain multiple more minute regions within these discriminative regions on middle-level feature channels.

Fine-Grained Image Classification General Classification

TLRM: Task-level Relation Module for GNN-based Few-Shot Learning

no code implementations25 Jan 2021 Yurong Guo, Zhanyu Ma, Xiaoxu Li, Yuan Dong

We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks.

Few-Shot Learning Relation +1

Knowledge Transfer Based Fine-grained Visual Classification

1 code implementation21 Dec 2020 Siqing Zhang, Ruoyi Du, Dongliang Chang, Zhanyu Ma, Jun Guo

Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions.

Classification Fine-Grained Image Classification +2

Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting

no code implementations15 Dec 2020 Wei Xu, Dingkang Liang, Yixiao Zheng, Zhanyu Ma

In this paper, we propose a simple yet efficient counting network based on point-level annotations.

Object Object Counting

BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification

1 code implementation29 Nov 2020 Xiaoxu Li, Jijie Wu, Zhuo Sun, Zhanyu Ma, Jie Cao, Jing-Hao Xue

Motivated by this, we propose a so-called \textit{Bi-Similarity Network} (\textit{BSNet}) that consists of a single embedding module and a bi-similarity module of two similarity measures.

Few-Shot Learning Fine-Grained Image Classification +1

Your "Flamingo" is My "Bird": Fine-Grained, or Not

1 code implementation CVPR 2021 Dongliang Chang, Kaiyue Pang, Yixiao Zheng, Zhanyu Ma, Yi-Zhe Song, Jun Guo

For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo".

Disentanglement Fine-Grained Image Classification +1

DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference

no code implementations17 Nov 2020 Jiyang Xie, Zhanyu Ma, Jing-Hao Xue, Guoqiang Zhang, Jun Guo

In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.

Actor and Action Modular Network for Text-based Video Segmentation

no code implementations2 Nov 2020 Jianhua Yang, Yan Huang, Kai Niu, Linjiang Huang, Zhanyu Ma, Liang Wang

Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of \emph{semantic asymmetry}.

Action Segmentation Action Understanding +5

GINet: Graph Interaction Network for Scene Parsing

1 code implementation ECCV 2020 Tianyi Wu, Yu Lu, Yu Zhu, Chuang Zhang, Ming Wu, Zhanyu Ma, Guodong Guo

GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.

Scene Parsing

SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification

no code implementations13 Sep 2020 Junhui Yin, Jiayan Qiu, Siqing Zhang, Zhanyu Ma, Jun Guo

To this end, we propose a Self-Supervised Knowledge Distillation (SSKD) technique containing two modules, the identity learning and the soft label learning.

Clustering Domain Adaptive Person Re-Identification +2

ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification

1 code implementation27 Jun 2020 Xiaoxu Li, Liyun Yu, Xiaochen Yang, Zhanyu Ma, Jing-Hao Xue, Jie Cao, Jun Guo

Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.

Classification General Classification +3

A Concise Review of Recent Few-shot Meta-learning Methods

no code implementations22 May 2020 Xiaoxu Li, Zhuo Sun, Jing-Hao Xue, Zhanyu Ma

Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge.

Meta-Learning

OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer

1 code implementation20 Apr 2020 Xiaoxu Li, Dongliang Chang, Zhanyu Ma, Zheng-Hua Tan, Jing-Hao Xue, Jie Cao, Jingyi Yu, Jun Guo

A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.

Classification General Classification

GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention

1 code implementation10 Mar 2020 Jiyang Xie, Dongliang Chang, Zhanyu Ma, Guo-Qiang Zhang, Jun Guo

In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.

Image Classification

Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization

1 code implementation9 Mar 2020 Junhui Yin, Siqing Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo

This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD).

Weakly-Supervised Object Localization

OVC-Net: Object-Oriented Video Captioning with Temporal Graph and Detail Enhancement

no code implementations8 Mar 2020 Fangyi Zhu, Jenq-Neng Hwang, Zhanyu Ma, Guang Chen, Jun Guo

Thereafter, we construct a new dataset, providing consistent object-sentence pairs, to facilitate effective cross-modal learning.

Object Sentence +1

Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation

2 code implementations8 Mar 2020 Dongliang Chang, Aneeshan Sain, Zhanyu Ma, Yi-Zhe Song, Jun Guo

The key insight lies with how we exploit the mutually beneficial information between two networks; (a) to separate samples of known and unknown classes, (b) to maximize the domain confusion between source and target domain without the influence of unknown samples.

Unsupervised Domain Adaptation

The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification

3 code implementations11 Feb 2020 Dongliang Chang, Yifeng Ding, Jiyang Xie, Ayan Kumar Bhunia, Xiaoxu Li, Zhanyu Ma, Ming Wu, Jun Guo, Yi-Zhe Song

The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.

Diversity Fine-Grained Image Classification +2

Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

no code implementations9 Feb 2020 Yifeng Ding, Shaoguo Wen, Jiyang Xie, Dongliang Chang, Zhanyu Ma, Zhongwei Si, Haibin Ling

Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.

Fine-Grained Image Classification General Classification

Competing Ratio Loss for Discriminative Multi-class Image Classification

1 code implementation25 Dec 2019 Ke Zhang, Yurong Guo, Xinsheng Wang, Dongliang Chang, Zhenbing Zhao, Zhanyu Ma, Tony X. Han

However, during the training of the deep convolutional neural network, the value of NLLR is not always positive or negative, which severely affects the convergence of NLLR.

Age Estimation Classification +3

Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval

no code implementations10 Nov 2019 Jianjun Lei, Yuxin Song, Bo Peng, Zhanyu Ma, Ling Shao, Yi-Zhe Song

How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR.

Retrieval Sketch-Based Image Retrieval

Competing Ratio Loss for Discriminative Multi-class Image Classification

no code implementations31 Jul 2019 Ke Zhang, Xinsheng Wang, Yurong Guo, Zhenbing Zhao, Zhanyu Ma, Tony X. Han

A lot of studies of image classification based on deep convolutional neural network focus on the network structure to improve the image classification performance.

Age Estimation Classification +3

Deep Zero-Shot Learning for Scene Sketch

no code implementations11 May 2019 Yao Xie, Peng Xu, Zhanyu Ma

We introduce a novel problem of scene sketch zero-shot learning (SSZSL), which is a challenging task, since (i) different from photo, the gap between common semantic domain (e. g., word vector) and sketch is too huge to exploit common semantic knowledge as the bridge for knowledge transfer, and (ii) compared with single-object sketch, more expressive feature representation for scene sketch is required to accommodate its high-level of abstraction and complexity.

Transfer Learning Zero-Shot Learning

Channel Max Pooling Layer for Fine-Grained Vehicle Classification

no code implementations14 Feb 2019 Zhanyu Ma, Dongliang Chang, Xiaoxu Li

Experimental results on two fine-grained vehicle datasets, the Stanford Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer could improve classification accuracies of deep neural networks on fine-grained vehicle classification in the situation that a massive of parameters are reduced.

Classification Fine-Grained Vehicle Classification +1

On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models

no code implementations13 Feb 2019 Zhanyu Ma, Jalil Taghia, Jun Guo

Recently, an improved framework, namely the extended variational inference (EVI), has been introduced and applied to derive analytically tractable solution by employing lower-bound approximation to the variational objective function.

Variational Inference

Language Identification with Deep Bottleneck Features

no code implementations18 Sep 2018 Zhanyu Ma, Hong Yu

In order to improve the SLD accuracy of short utterances a phase vocoder based time-scale modification(TSM) method is used to reduce and increase speech rated of the test utterance.

Language Identification Speech Language Identification +1

Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency

no code implementations2 Aug 2018 Zhanyu Ma

In this paper, we continue our previous work on the Dirichlet mixture model (DMM)-based VQ to derive the performance bound of the LSF VQ.

Quantization

Deep Neural Network for Analysis of DNA Methylation Data

no code implementations2 Aug 2018 Hong Yu, Zhanyu Ma

Many researches demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer.

Histogram Transform-based Speaker Identification

no code implementations2 Aug 2018 Zhanyu Ma, Hong Yu

A novel text-independent speaker identification (SI) method is proposed.

Speaker Identification

Impacts of Weather Conditions on District Heat System

no code implementations2 Aug 2018 Jiyang Xie, Zhanyu Ma, Jun Guo

Using artificial neural network for the prediction of heat demand has attracted more and more attention.

Classification of EEG Signal based on non-Gaussian Neutral Vector

no code implementations2 Aug 2018 Zhanyu Ma

In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task.

Brain Computer Interface Classification +4

Mobile big data analysis with machine learning

no code implementations2 Aug 2018 Jiyang Xie, Zeyu Song, Yupeng Li, Zhanyu Ma

Finally, we summarize the main challenges and future development directions of mobile big data analysis.

BIG-bench Machine Learning speech-recognition +1

SEA: A Combined Model for Heat Demand Prediction

no code implementations28 Jul 2018 Jiyang Xie, Jiaxin Guo, Zhanyu Ma, Jing-Hao Xue, Qie Sun, Hailong Li, Jun Guo

ENN and ARIMA are used to predict seasonal and trend components, respectively.

Infinite Mixture of Inverted Dirichlet Distributions

no code implementations27 Jul 2018 Zhanyu Ma, Yuping Lai

In this work, we develop a novel Bayesian estimation method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements.

Variational Inference

BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint

no code implementations8 Jul 2018 Jiyang Xie, Zhanyu Ma, Guo-Qiang Zhang, Jing-Hao Xue, Jen-Tzung Chien, Zhiqing Lin, Jun Guo

In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.

Fine-Grained Age Estimation in the wild with Attention LSTM Networks

no code implementations26 May 2018 Ke Zhang, Na Liu, Xingfang Yuan, Xinyao Guo, Ce Gao, Zhenbing Zhao, Zhanyu Ma

Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images.

Ranked #4 on Age And Gender Classification on Adience Age (using extra training data)

Age And Gender Classification Age Estimation +1

SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval

1 code implementation CVPR 2018 Peng Xu, Yongye Huang, Tongtong Yuan, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Zhanyu Ma, Jun Guo

Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.

Deep Hashing Sketch Recognition

Decorrelation of Neutral Vector Variables: Theory and Applications

no code implementations30 May 2017 Zhanyu Ma, Jing-Hao Xue, Arne Leijon, Zheng-Hua Tan, Zhen Yang, Jun Guo

In this paper, we propose novel strategies for neutral vector variable decorrelation.

DNN Filter Bank Cepstral Coefficients for Spoofing Detection

no code implementations13 Feb 2017 Hong Yu, Zheng-Hua Tan, Zhanyu Ma, Jun Guo

In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech.

Speaker Verification Speech Synthesis

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