Search Results for author: Ya Li

Found 19 papers, 5 papers with code

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

no code implementations ICML 2020 Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian

To obtain sufficient knowledge for crafting adversarial examples, previous methods query the target model with inputs that are perturbed with different searching directions.

A Keypoint Based Enhancement Method for Audio Driven Free View Talking Head Synthesis

no code implementations7 Oct 2022 Yichen Han, Ya Li, Yingming Gao, Jinlong Xue, Songpo Wang, Lei Yang

Then we used keypoint decomposition to extract video synthesis controlling parameters from the backend output and the source image.

Towards Lightweight Black-Box Attacks against Deep Neural Networks

no code implementations29 Sep 2022 Chenghao Sun, Yonggang Zhang, Wan Chaoqun, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian

As it is hard to mitigate the approximation error with few available samples, we propose Error TransFormer (ETF) for lightweight attacks.

ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis

no code implementations20 Mar 2022 Jinlong Xue, Yayue Deng, Yichen Han, Ya Li, Jianqing Sun, Jiaen Liang

In recent years, neural network based methods for multi-speaker text-to-speech synthesis (TTS) have made significant progress.

Speaker Verification Speech Synthesis +1

Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking

1 code implementation CVPR 2020 Hongjun Wang, Guangrun Wang, Ya Li, Dongyu Zhang, Liang Lin

To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e. g., the criminals may use the adversarial perturbations to cheat the CCTV systems.

Adversarial Attack Person Re-Identification

Speech Emotion Recognition via Contrastive Loss under Siamese Networks

no code implementations23 Oct 2019 Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang

It outperforms the baseline system that is optimized without the contrastive loss function with 1. 14% and 2. 55% in the weighted accuracy and the unweighted accuracy, respectively.

Speech Emotion Recognition

Expression Analysis Based on Face Regions in Read-world Conditions

no code implementations23 Oct 2019 Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang, Ming-Yue Niu

To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.

Facial Emotion Recognition Facial Expression Recognition

On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning

no code implementations3 Apr 2019 Ya Li, Xinmei Tian, Tongliang Liu, DaCheng Tao

The objective of our proposed method is to transform the features from different tasks into a common feature space in which the tasks are closely related and the shared parameters can be better optimized.

Multi-Task Learning

Learning Efficient Lexically-Constrained Neural Machine Translation with External Memory

no code implementations31 Jan 2019 Ya Li, Xinyu Liu, Dan Liu, Xueqiang Zhang, Junhua Liu

Recent years has witnessed dramatic progress of neural machine translation (NMT), however, the method of manually guiding the translation procedure remains to be better explored.

Machine Translation NMT +1

Deep Domain Generalization via Conditional Invariant Adversarial Networks

no code implementations ECCV 2018 Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, DaCheng Tao

Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$.

Domain Generalization Representation Learning

Domain Generalization via Conditional Invariant Representation

1 code implementation23 Jul 2018 Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, DaCheng Tao

With the conditional invariant representation, the invariance of the joint distribution $\mathbb{P}(h(X), Y)$ can be guaranteed if the class prior $\mathbb{P}(Y)$ does not change across training and test domains.

Domain Generalization

Cost-Effective Active Learning for Deep Image Classification

1 code implementation13 Jan 2017 Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, Liang Lin

In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.

Active Learning Classification +5

DARI: Distance metric And Representation Integration for Person Verification

no code implementations15 Apr 2016 Guangrun Wang, Liang Lin, Shengyong Ding, Ya Li, Qing Wang

The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately.

Ranked #7 on Person Re-Identification on SYSU-30k (using extra training data)

Metric Learning Person Re-Identification +1

Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention

no code implementations28 Mar 2016 Linlin Chao, Jian-Hua Tao, Minghao Yang, Ya Li, Zhengqi Wen

The other one is locating and re-weighting the perception attentions in the whole audio-visual stream for better recognition.

Classification Emotion Recognition +1

Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy

no code implementations8 Aug 2015 Zhanglin Peng, Ya Li, Zhaoquan Cai, Liang Lin

In each layer, we construct a dictionary of filters by combining the filters from the lower layer, and iteratively optimize the image representation with a joint discriminative-generative formulation, i. e. minimization of empirical classification error plus regularization of analysis image generation over training images.

Classification Dictionary Learning +3

Defuzzify firstly or finally: Dose it matter in fuzzy DEMATEL under uncertain environment?

no code implementations20 Mar 2014 Yunpeng Li, Ya Li, Jie Liu, Yong Deng

The results of defuzzification at the first step are not coincide with the results of defuzzification at the final step. It seems that the alternative is to defuzzification in the final step in fuzzy DEMATEL.

Decision Making

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