1 code implementation • 16 Mar 2024 • Ke Lin, Yiyang Luo, Zijian Zhang, Ping Luo
Generative linguistic steganography attempts to hide secret messages into covertext.
no code implementations • 15 Mar 2024 • Yiyang Luo, Ke Lin, Chao Gu
The proliferation of large language models (LLMs) in generating content raises concerns about text copyright.
no code implementations • 26 Nov 2023 • Yiyang Luo, Ke Lin
Indoor scene augmentation has become an emerging topic in the field of computer vision with applications in augmented and virtual reality.
no code implementations • 4 Mar 2022 • Ke Lin, Yong A, Zhuoxin Gan, Yingying Jiang
To increase the correctness of the evaluation of architectures, besides direct evaluation using the inherited weights, we further apply a few-shot predictor to assess the architecture on the other hand.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Ke Lin, Zhuoxin Gan, LiWei Wang
In the proposed study, we make the first attempt to train the video captioning model on labeled data and unlabeled data jointly, in a semi-supervised learning manner.
1 code implementation • 7 Jun 2020 • Zhiguo Wang, Liusha Yang, Feng Yin, Ke Lin, Qingjiang Shi, Zhi-Quan Luo
In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine.
no code implementations • 5 Jun 2020 • Ke Lin, Zhuoxin Gan, Li-Wei Wang
This report describes our model for VATEX Captioning Challenge 2020.
2 code implementations • 31 Aug 2019 • Haoran Chen, Ke Lin, Alexander Maye, Jianming Li, Xiaolin Hu
Given the features of a video, recurrent neural networks can be used to automatically generate a caption for the video.
no code implementations • EACL 2017 • Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, Lori Levin
We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics.