1 code implementation • 21 Apr 2024 • Tao Feng, Lizhen Qu, Zhuang Li, Haolan Zhan, Yuncheng Hua, Gholamreza Haffari
Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains.
2 code implementations • 12 Mar 2024 • Weijia Wu, Zhuang Li, YuChao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, Di Zhang
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.
1 code implementation • 8 Mar 2024 • Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, Zeyu Wang
We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device.
1 code implementation • 6 Mar 2024 • Zijie Zeng, Shiqi Liu, Lele Sha, Zhuang Li, Kaixun Yang, Sannyuya Liu, Dragan Gašević, Guanliang Chen
Our empirical findings highlight (1) detecting AI-generated sentences in hybrid texts is overall a challenging task because (1. 1) human writers' selecting and even editing AI-generated sentences based on personal preferences adds difficulty in identifying the authorship of segments; (1. 2) the frequent change of authorship between neighboring sentences within the hybrid text creates difficulties for segment detectors in identifying authorship-consistent segments; (1. 3) the short length of text segments within hybrid texts provides limited stylistic cues for reliable authorship determination; (2) before embarking on the detection process, it is beneficial to assess the average length of segments within the hybrid text.
no code implementations • 29 Feb 2024 • Anton Lozhkov, Raymond Li, Loubna Ben allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #25 on Code Generation on MBPP
no code implementations • 17 Feb 2024 • Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Lay-Ki Soon, Zhaleh Semnani Azad, Ingrid Zukerman, Gholamreza Haffari
While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.
no code implementations • 7 Feb 2024 • Zhuang Li, Levon Haroutunian, Raj Tumuluri, Philip Cohen, Gholamreza Haffari
Post-editing has proven effective in improving the quality of text generated by large language models (LLMs) such as GPT-3. 5 or GPT-4, particularly when direct updating of their parameters to enhance text quality is infeasible or expensive.
no code implementations • 2 Feb 2024 • Haolan Zhan, YuFei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Gholamreza Haffari
Negotiation is a crucial ability in human communication.
1 code implementation • 29 Jan 2024 • Yuncheng Hua, Zhuang Li, Linhao Luo, Kadek Ananta Satriadi, Tao Feng, Haolan Zhan, Lizhen Qu, Suraj Sharma, Ingrid Zukerman, Zhaleh Semnani-Azad, Gholamreza Haffari
We have released our code and software at:~\url{https://github. com/AnonymousEACLDemo/SADAS}.
no code implementations • 11 Jan 2024 • Aditya Joshi, Raj Dabre, Diptesh Kanojia, Zhuang Li, Haolan Zhan, Gholamreza Haffari, Doris Dippold
Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
1 code implementation • 18 Dec 2023 • Yuyang Chai, Zhuang Li, Jiahui Liu, Lei Chen, Fei Li, Donghong Ji, Chong Teng
Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines.
1 code implementation • 8 Dec 2023 • Tongxin Hu, Zhuang Li, Xin Jin, Lizhen Qu, Xin Zhang
Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms.
1 code implementation • 29 Nov 2023 • Weijia Wu, Yuzhong Zhao, Zhuang Li, Lianlei Shan, Hong Zhou, Mike Zheng Shou
Image segmentation based on continual learning exhibits a critical drop of performance, mainly due to catastrophic forgetting and background shift, as they are required to incorporate new classes continually.
1 code implementation • 24 Nov 2023 • Weijia Wu, Zhuang Li, Yefei He, Mike Zheng Shou, Chunhua Shen, Lele Cheng, Yan Li, Tingting Gao, Di Zhang, Zhongyuan Wang
In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation.
no code implementations • 21 Sep 2023 • Levon Haroutunian, Zhuang Li, Lucian Galescu, Philip Cohen, Raj Tumuluri, Gholamreza Haffari
Our approach involves initially generating a set of candidate outputs by prompting an LLM and subsequently reranking them using a task-specific reranker model.
no code implementations • 14 Sep 2023 • Zhuang Li
This thesis explores challenges in semantic parsing, specifically focusing on scenarios with limited data and computational resources.
1 code implementation • 27 May 2023 • Zhuang Li, Yuyang Chai, Terry Yue Zhuo, Lizhen Qu, Gholamreza Haffari, Fei Li, Donghong Ji, Quan Hung Tran
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
Ranked #2 on Human Judgment Correlation on Flickr8k-Expert
no code implementations • 22 May 2023 • Zhuang Li, Lizhen Qu, Philip R. Cohen, Raj V. Tumuluri, Gholamreza Haffari
Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem.
1 code implementation • 5 May 2023 • Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Hong Zhou, Mike Zheng Shou, Xiang Bai
Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i. e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video.
1 code implementation • 5 May 2023 • Yuzhong Zhao, Weijia Wu, Zhuang Li, Jiahong Li, Weiqiang Wang
This paper introduces a novel video text synthesis technique called FlowText, which utilizes optical flow estimation to synthesize a large amount of text video data at a low cost for training robust video text spotters.
1 code implementation • 24 Apr 2023 • Haolan Zhan, Zhuang Li, YuFei Wang, Linhao Luo, Tao Feng, Xiaoxi Kang, Yuncheng Hua, Lizhen Qu, Lay-Ki Soon, Suraj Sharma, Ingrid Zukerman, Zhaleh Semnani-Azad, Gholamreza Haffari
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction Synthetic Data Generation
no code implementations • 10 Apr 2023 • Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Mike Zheng Shou, Umapada Pal, Dimosthenis Karatzas, Xiang Bai
In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios.
no code implementations • 30 Jan 2023 • Terry Yue Zhuo, Zhuang Li, Yujin Huang, Fatemeh Shiri, Weiqing Wang, Gholamreza Haffari, Yuan-Fang Li
Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question.
no code implementations • 30 Jan 2023 • Zhuang Li, Gholamreza Haffari
Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language.
no code implementations • 18 Dec 2022 • Haolan Zhan, YuFei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Gholamreza Haffari
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
2 code implementations • 28 Sep 2022 • Bo Yan, Fengliang Qi, Zhuang Li, Yadong Li, Hongbin Wang
The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation Challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court.
1 code implementation • 18 Jul 2022 • Wejia Wu, Zhuang Li, Jiahong Li, Chunhua Shen, Hong Zhou, Size Li, Zhongyuan Wang, Ping Luo
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
no code implementations • 28 Jun 2022 • Bo Yan, Leilei Cao, Zhuang Li, Hongbin Wang
Finally, our approach achieves 63. 008\%AP@0. 50:0. 95 on the test set of CVPR2022 AVA Challenge.
no code implementations • 24 Jun 2022 • Leilei Cao, Zhuang Li, Bo Yan, Feng Zhang, Fengliang Qi, Yuchen Hu, Hongbin Wang
The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames.
no code implementations • 25 May 2022 • Zhenhua Wang, Ming Ren, Dong Gao, Zhuang Li
The Zipf's Law emerges as a well-suited adoption, and to transition from words to entities, words within the documents are classified as common and rare ones.
no code implementations • 21 Mar 2022 • Fatemeh Shiri, Terry Yue Zhuo, Zhuang Li, Van Nguyen, Shirui Pan, Weiqing Wang, Reza Haffari, Yuan-Fang Li
In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.
1 code implementation • 27 Feb 2022 • Zhuang Li, Lizhen Qu, Qiongkai Xu, Tongtong Wu, Tianyang Zhan, Gholamreza Haffari
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples.
1 code implementation • 30 Dec 2021 • Zhuang Li, Weijia Wu, Mike Zheng Shou, Jiahong Li, Size Li, Zhongyuan Wang, Hong Zhou
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
3 code implementations • 9 Dec 2021 • Weijia Wu, Yuanqiang Cai, Debing Zhang, Sibo Wang, Zhuang Li, Jiahong Li, Yejun Tang, Hong Zhou
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
no code implementations • ICLR 2022 • Tongtong Wu, Massimo Caccia, Zhuang Li, Yuan-Fang Li, Guilin Qi, Gholamreza Haffari
In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 veins of CL methods on 3 benchmarks in 2 typical incremental settings.
1 code implementation • EMNLP 2021 • Zhuang Li, Lizhen Qu, Gholamreza Haffari
We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.
1 code implementation • 11 Jun 2021 • Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang
In our evaluations, the method outperforms all the state-of-the-art image retrieval algorithms on some out-of-domain image datasets.
no code implementations • 10 Apr 2021 • Zhuang Li
The experiments have demonstrated that this type of feature, combine with the traditional hand-crafted features, could improve the performance of the logistic classification model for relation extraction, especially on the classification of relations with only minor training instances.
no code implementations • EACL 2021 • Shuo Huang, Zhuang Li, Lizhen Qu, Lei Pan
In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.
1 code implementation • EACL 2021 • Zhuang Li, Lizhen Qu, Shuo Huang, Gholamreza Haffari
In this work, we investigate the problems of semantic parsing in a few-shot learning setting.
1 code implementation • COLING 2020 • Zhuang Li, Lizhen Qu, Gholamreza Haffari
Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.
no code implementations • 21 Nov 2019 • Badong Chen, Yuqing Xie, Zhuang Li, Yingsong Li, Pengju Ren
Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables.