no code implementations • 23 Apr 2024 • mengqun Jin, Zexuan Qiu, Jieming Zhu, Zhenhua Dong, Xiu Li
Finally, we train and test semantic code with with generative retrieval on a sequential recommendation model.
1 code implementation • 6 Mar 2024 • Zexuan Qiu, Jingjing Li, Shijue Huang, Wanjun Zhong, Irwin King
Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese.
no code implementations • 14 Jan 2024 • Zexuan Qiu, Jiahong Liu, Yankai Chen, Irwin King
Existing unsupervised deep product quantization methods primarily aim for the increased similarity between different views of the identical image, whereas the delicate multi-level semantic similarities preserved between images are overlooked.
no code implementations • 8 May 2023 • Zenan Xu, Xiaojun Meng, Yasheng Wang, Qinliang Su, Zexuan Qiu, Xin Jiang, Qun Liu
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript.
1 code implementation • 31 Oct 2022 • Zexuan Qiu, Qinliang Su, Jianxing Yu, Shijing Si
Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document distances.
1 code implementation • 13 May 2021 • Zexuan Qiu, Qinliang Su, Zijing Ou, Jianxing Yu, Changyou Chen
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible.