1 code implementation • 22 Dec 2023 • Rongao Li, Jie Fu, Bo-Wen Zhang, Tao Huang, Zhihong Sun, Chen Lyu, Guang Liu, Zhi Jin, Ge Li
Moreover, each TACO problem includes several fine-grained labels such as task topics, algorithms, programming skills, and difficulty levels, providing a more precise reference for the training and evaluation of code generation models.
Ranked #1 on Code Generation on TACO-Code
no code implementations • 13 Dec 2023 • Zhenduo Zhang, Bo-Wen Zhang, Guang Liu
Current text-to-image editing models often encounter challenges with smoothly manipulating multiple attributes using a single instruction.
1 code implementation • 12 Nov 2022 • Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
1 code implementation • 6 Feb 2020 • Akshay Rangesh, Bo-Wen Zhang, Mohan M. Trivedi
GPCycleGAN is based on the well-known CycleGAN approach - with the addition of a gaze classifier and a gaze consistency loss for additional supervision.
1 code implementation • 25 Oct 2019 • Mauro Barni, Ehsan Nowroozi, Benedetta Tondi, Bo-Wen Zhang
We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features.
no code implementations • 5 Sep 2019 • Zhichen Zhao, Bo-Wen Zhang, Yuning Jiang, Li Xu, Lei LI, Wei-Ying Ma
However, the datasets from source domain are simply discarded in the fine-tuning process.
no code implementations • 19 Aug 2019 • Melissa Ailem, Bo-Wen Zhang, Fei Sha
In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document.
no code implementations • EMNLP 2018 • Melissa Ailem, Bo-Wen Zhang, Aurelien Bellet, Pascal Denis, Fei Sha
Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data.
no code implementations • WS 2017 • Zan-Xia Jin, Bo-Wen Zhang, Fan Fang, Le-Le Zhang, Xu-Cheng Yin
This paper describes the participation of USTB{\_}PRIR team in the 2017 BioASQ 5B on question answering, including document retrieval, snippet retrieval, and concept retrieval task.
1 code implementation • 1 Sep 2016 • Zhe Wang, Li-Min Wang, Yali Wang, Bo-Wen Zhang, Yu Qiao
In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition.
1 code implementation • 2 Aug 2016 • Yuanjun Xiong, Li-Min Wang, Zhe Wang, Bo-Wen Zhang, Hang Song, Wei Li, Dahua Lin, Yu Qiao, Luc van Gool, Xiaoou Tang
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016.