no code implementations • COLING 2022 • Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu
Recently, many task-oriented dialogue systems need to serve users in different languages.
no code implementations • 22 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.
no code implementations • 10 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.
1 code implementation • 25 Aug 2024 • Haiwen Zhang, Zixi Yang, Yuanzhi Liu, Xinran Wang, Zheqi He, Kongming Liang, Zhanyu Ma
Currently, large vision-language models have gained promising progress on many downstream tasks.
1 code implementation • 29 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.
1 code implementation • 19 Jun 2024 • Mengqiu Xu, Ming Wu, Kaixin Chen, Yixiang Huang, Mingrui Xu, Yujia Yang, Yiqing Feng, Yiying Guo, Bin Huang, Dongliang Chang, Zhenwei Shi, Chuang Zhang, Zhanyu Ma, Jun Guo
Marine fog poses a significant hazard to global shipping, necessitating effective detection and forecasting to reduce economic losses.
no code implementations • CVPR 2024 • Yufei Han, Heng Guo, Koki Fukai, Hiroaki Santo, Boxin Shi, Fumio Okura, Zhanyu Ma, Yunpeng Jia
We present NeRSP, a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images.
1 code implementation • 5 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.
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.
no code implementations • 12 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.
1 code implementation • 26 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.
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.
1 code implementation • 18 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.
Ranked #1 on Generalized Zero Shot skeletal action recognition on NTU RGB+D (using extra training data)
no code implementations • 5 Aug 2023 • Zhanyu Ma, Jian Ye, Shuang Cheng
The model consists of an additional layer of latent dialogue action.
1 code implementation • 13 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).
1 code implementation • CVPR 2023 • Ruoyi Du, Dongliang Chang, Kongming Liang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
Our code is available at https://github. com/PRIS-CV/On-the-fly-Category-Discovery.
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.
no code implementations • ICCV 2023 • Yurong Guo, Ruoyi Du, Yuan Dong, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
In this paper, we first observe the dependence of task-specific parameter configuration on the target task.
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
1 code implementation • 30 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).
1 code implementation • 15 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.
1 code implementation • 2 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.
1 code implementation • 1 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.
1 code implementation • 18 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.
no code implementations • 20 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.
1 code implementation • 6 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.
1 code implementation • 6 Dec 2021 • Dongliang Chang, Kaiyue Pang, Ruoyi Du, Zhanyu Ma, Yi-Zhe Song, Jun Guo
1 lays out our approach in answering this question.
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
no code implementations • 21 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.
1 code implementation • 16 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.
1 code implementation • 16 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.
no code implementations • 7 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.
no code implementations • 17 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.
no code implementations • 1 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.
no code implementations • 1 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
1 code implementation • 11 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.
2 code implementations • 31 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.
no code implementations • 25 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.
no code implementations • 24 Jan 2021 • Shuai Xu, Dongliang Chang, Jiyang Xie, Zhanyu Ma
The proposed method outperforms the SOTA attention modules in the FGVC task.
Ranked #23 on Fine-Grained Image Classification on FGVC Aircraft
1 code implementation • 21 Jan 2021 • Tian Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category.
Ranked #34 on Fine-Grained Image Classification on FGVC Aircraft
1 code implementation • 21 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.
Ranked #39 on Fine-Grained Image Classification on CUB-200-2011
no code implementations • 15 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.
1 code implementation • 29 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.
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".
Ranked #18 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 17 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.
no code implementations • 2 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}.
Ranked #9 on Referring Expression Segmentation on J-HMDB
no code implementations • 12 Oct 2020 • Zeyu Song, Dongliang Chang, Zhanyu Ma, Xiaoxu Li, Zheng-Hua Tan
The loss function is a key component in deep learning models.
1 code implementation • 11 Oct 2020 • Jiyang Xie, Zhanyu Ma, and Jianjun Lei, Guoqiang Zhang, Jing-Hao Xue, Zheng-Hua Tan, Jun Guo
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
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.
no code implementations • 13 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.
1 code implementation • 27 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.
no code implementations • 22 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.
1 code implementation • 20 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.
1 code implementation • 10 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.
1 code implementation • 9 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).
no code implementations • 8 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.
2 code implementations • 8 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.
5 code implementations • ECCV 2020 • Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Zhanyu Ma, Yi-Zhe Song, Jun Guo
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #21 on Fine-Grained Image Classification on Stanford Cars
no code implementations • 21 Feb 2020 • Peng Xu, Kun Liu, Tao Xiang, Timothy M. Hospedales, Zhanyu Ma, Jun Guo, Yi-Zhe Song
Existing sketch-analysis work studies sketches depicting static objects or scenes.
3 code implementations • 11 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.
Ranked #31 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 9 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.
1 code implementation • 25 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.
no code implementations • 10 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.
no code implementations • 31 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.
no code implementations • 11 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.
no code implementations • 14 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.
no code implementations • 13 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.
no code implementations • 18 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.
no code implementations • 2 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.
no code implementations • 2 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.
no code implementations • 2 Aug 2018 • Zhanyu Ma, Hong Yu
A novel text-independent speaker identification (SI) method is proposed.
no code implementations • 2 Aug 2018 • Jiyang Xie, Zhanyu Ma, Jun Guo
Using artificial neural network for the prediction of heat demand has attracted more and more attention.
no code implementations • 2 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.
no code implementations • 2 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.
no code implementations • 28 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.
no code implementations • 27 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.
no code implementations • 8 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.
no code implementations • 26 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)
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.
no code implementations • 30 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.
no code implementations • 28 May 2017 • Peng Xu, Qiyue Yin, Yongye Huang, Yi-Zhe Song, Zhanyu Ma, Liang Wang, Tao Xiang, W. Bastiaan Kleijn, Jun Guo
Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo.
Ranked #5 on Sketch-Based Image Retrieval on Chairs
no code implementations • 13 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.