no code implementations • AACL (iwdp) 2020 • Vicente Ivan Sanchez Carmona, Yibing Yang, Ziyue Wen, Ruosen Li, Xiaohua Wang, Changjian Hu
In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation.
no code implementations • 23 Feb 2024 • Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Jianhao Zhu, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters.
no code implementations • 26 Dec 2023 • Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Jianhao Zhu, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness.
1 code implementation • 14 Dec 2023 • Jinguo Zhu, Xiaohan Ding, Yixiao Ge, Yuying Ge, Sijie Zhao, Hengshuang Zhao, Xiaohua Wang, Ying Shan
In combination with the existing text tokenizer and detokenizer, this framework allows for the encoding of interleaved image-text data into a multimodal sequence, which can subsequently be fed into the transformer model.
no code implementations • 16 Nov 2023 • Yimin Jing, Renren Jin, Jiahao Hu, Huishi Qiu, Xiaohua Wang, Peng Wang, Deyi Xiong
In pursuit of this goal, various benchmarks have been constructed to evaluate the instruction-following capacity of these models.
2 code implementations • CVPR 2023 • Hao Li, Jinguo Zhu, Xiaohu Jiang, Xizhou Zhu, Hongsheng Li, Chun Yuan, Xiaohua Wang, Yu Qiao, Xiaogang Wang, Wenhai Wang, Jifeng Dai
In this paper, we propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance.
1 code implementation • 9 Jun 2022 • Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai
To mitigate such interference, we introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models.
1 code implementation • CVPR 2022 • Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Xiaogang Wang, Hongsheng Li, Xiaohua Wang, Jifeng Dai
The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage.
1 code implementation • CVPR 2021 • Jinguo Zhu, Shixiang Tang, Dapeng Chen, Shijie Yu, Yakun Liu, Aijun Yang, Mingzhe Rong, Xiaohua Wang
Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation.
Ranked #26 on Knowledge Distillation on ImageNet
no code implementations • 29 Nov 2018 • Xiaohua Wang, Muzi Peng, Lijuan Pan, Min Hu, Chunhua Jin, Fuji Ren
In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images.