no code implementations • Findings (ACL) 2022 • Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu
As like previous work, we rely on negative entities to encourage our model to discriminate the golden entities during training.
no code implementations • 28 Dec 2024 • Jiaming Yan, Jianchun Liu, Hongli Xu, Liusheng Huang, Jiantao Gong, Xudong Liu, Kun Hou
For the global model download, we design a greedy method to optimize the compression ratio for each device based on the staleness of the local model, ensuring a precise initial model for local training.
1 code implementation • 27 Dec 2024 • Xiaohan Zhang, Xudong Mou, Rui Wang, Tianyu Wo, Ningbo Gu, Tiejun Wang, Cangbai Xu, Xudong Liu
This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators.
1 code implementation • 20 Dec 2024 • Qianren Mao, Yangyifei Luo, Jinlong Zhang, Hanwen Hao, Zhilong Cao, Xiaolong Wang, Xiao Guan, Zhenting Huang, Weifeng Jiang, Shuyu Guo, Zhentao Han, Qili Zhang, Siyuan Tao, Yujie Liu, Junnan Liu, Zhixing Tan, Jie Sun, Bo Li, Xudong Liu, Richong Zhang, JianXin Li
As the complexity of RAG systems continues to escalate, we underscore the critical need to identify potential failure points in RAG systems.
1 code implementation • 26 Jun 2024 • Zhijie Nie, Richong Zhang, Zhangchi Feng, Hailang Huang, Xudong Liu
The methods with cross-modal style suffer from the inter-modal optimization direction bias, resulting in inconsistent rank across languages within each instance, which cannot be reflected by Recall@K. To solve these problems, we propose a simple but effective 1-to-K contrastive learning method, which treats each language equally and eliminates error propagation and optimization bias.
1 code implementation • 4 Jun 2024 • Philip Anastassiou, Jiawei Chen, Jitong Chen, Yuanzhe Chen, Zhuo Chen, Ziyi Chen, Jian Cong, Lelai Deng, Chuang Ding, Lu Gao, Mingqing Gong, Peisong Huang, Qingqing Huang, Zhiying Huang, YuanYuan Huo, Dongya Jia, ChuMin Li, Feiya Li, Hui Li, Jiaxin Li, Xiaoyang Li, Xingxing Li, Lin Liu, Shouda Liu, Sichao Liu, Xudong Liu, Yuchen Liu, Zhengxi Liu, Lu Lu, Junjie Pan, Xin Wang, Yuping Wang, Yuxuan Wang, Zhen Wei, Jian Wu, Chao Yao, Yifeng Yang, YuanHao Yi, Junteng Zhang, Qidi Zhang, Shuo Zhang, Wenjie Zhang, Yang Zhang, Zilin Zhao, Dejian Zhong, Xiaobin Zhuang
Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild.
no code implementations • CVPR 2024 • Kejia Yin, Varshanth R. Rao, Ruowei Jiang, Xudong Liu, Parham Aarabi, David B. Lindell
Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations to identify sparse facial landmarks in the absence of annotated data.
1 code implementation • CVPR 2024 • Ziyi Wu, Mathias Gehrig, Qing Lyu, Xudong Liu, Igor Gilitschenski
On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
1 code implementation • 9 Sep 2023 • Zhijie Nie, Richong Zhang, Zhongyuan Wang, Xudong Liu
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical applications.
1 code implementation • ICCV 2023 • Wenjie Yang, Yiyi Chen, Yan Li, Yanhua Cheng, Xudong Liu, Quan Chen, Han Li
Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products.
1 code implementation • 1 Aug 2023 • Xuan-Bac Nguyen, Xudong Liu, Xin Li, Khoa Luu
The goal is to predict brain responses across the entire visual brain, as it is the region where the most reliable responses to images have been observed.
1 code implementation • 10 Jun 2023 • Ziyi Wu, Xudong Liu, Igor Gilitschenski
Recent advances in zero-shot and few-shot classification heavily rely on the success of pre-trained vision-language models (VLMs) such as CLIP.
1 code implementation • CVPR 2023 • Tingting Liao, Xiaomei Zhang, Yuliang Xiu, Hongwei Yi, Xudong Liu, Guo-Jun Qi, Yong Zhang, Xuan Wang, Xiangyu Zhu, Zhen Lei
This paper presents a framework for efficient 3D clothed avatar reconstruction.
1 code implementation • 1 Apr 2023 • Binhang Qi, Hailong Sun, Xiang Gao, Hongyu Zhang, Zhaotian Li, Xudong Liu
Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e. g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack.
no code implementations • 28 Feb 2023 • Kai Sun, Richong Zhang, Samuel Mensah, Nikolaos Aletras, Yongyi Mao, Xudong Liu
Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance.
no code implementations • 18 Feb 2023 • Na Zhang, Xudong Liu, Xin Li, Guo-Jun Qi
Semantic face image manipulation has received increasing attention in recent years.
1 code implementation • 4 Jul 2022 • Rui Wang, Chongwei Liu, Xudong Mou, Kai Gao, Xiaohui Guo, Pin Liu, Tianyu Wo, Xudong Liu
To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification.
no code implementations • 22 Jan 2022 • Ying Wang, Chiuman Ho, Wenju Xu, Ziwei Xuan, Xudong Liu, Guo-Jun Qi
We propose a Dual-Flattening Transformer (DFlatFormer) to enable high-resolution output by reducing complexity to $\mathcal{O}(hw(H+W))$ that is multiple orders of magnitude smaller than the naive dense transformer.
no code implementations • 6 Aug 2021 • Dengfeng Ke, Yuxing Lu, Xudong Liu, Yanyan Xu, Jing Sun, Cheng-Hao Cai
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production.
no code implementations • 23 Oct 2020 • Yunjie Zhang, Fei Tao, Xudong Liu, Runze Su, Xiaorong Mei, Weicong Ding, Zhichen Zhao, Lei Yuan, Ji Liu
In this paper, we proposed a novel end-to-end self-organizing framework for user behavior prediction.
1 code implementation • 14 Oct 2020 • Dong Li, Sitong Chen, Xudong Liu, YunDa Sun, Li Zhang
In this paper, we propose a balanced filter pruning method for both performance and pruning speed.
no code implementations • 14 Sep 2020 • Runze Su, Fei Tao, Xudong Liu, Hao-Ran Wei, Xiaorong Mei, Zhiyao Duan, Lei Yuan, Ji Liu, Yuying Xie
The applications of short-term user-generated video (UGV), such as Snapchat, and Youtube short-term videos, booms recently, raising lots of multimodal machine learning tasks.
no code implementations • EMNLP 2020 • Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task.
Ranked #8 on
Relation Extraction
on WebNLG
1 code implementation • CVPR 2020 • Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, Jungong Han
Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner.
Ranked #20 on
Cross-Modal Retrieval
on Flickr30k
1 code implementation • 8 Dec 2019 • Xudong Liu, Ruizhe Wang, Chih-Fan Chen, Minglei Yin, Hao Peng, Shukhan Ng, Xin Li
Inspired by the latest advances in style-based synthesis and face beauty prediction, we propose a novel framework of face beautification.
no code implementations • 7 Dec 2019 • Ruizhe Wang, Chih-Fan Chen, Hao Peng, Xudong Liu, Oliver Liu, Xin Li
We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image.
no code implementations • IJCNLP 2019 • Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence.
no code implementations • 25 Sep 2019 • Xudong Liu, Christian Fritz, Matthew Klenk
Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice.
no code implementations • 19 Sep 2019 • Joseph Allen, Ahmed Moussa, Xudong Liu
In this work, we present a novel human-in-the-loop framework to help the human user understand the decision making process that involves choosing preferred options.
no code implementations • 19 Sep 2019 • Ahmed Moussa, Xudong Liu
We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists).
no code implementations • 30 Jan 2019 • Xudong Liu, Tao Li, Hao Peng, Iris Chuoying Ouyang, Taehwan Kim, Ruizhe Wang
The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability.
no code implementations • 2 Oct 2018 • Tao Li, Xudong Liu, Shih-An Su
Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics.
no code implementations • 23 May 2018 • Xudong Liu, Guodong Guo
To address this question, we deploy deep training for facial attributes prediction, and we explore the inconsistency issue among the attributes computed from each single image.
no code implementations • 29 Oct 2014 • Xudong Liu, Bin Zhang, Ting Zhang, Chang Liu
Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM).
no code implementations • 29 Nov 2013 • Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.