no code implementations • 8 Apr 2024 • Xiaoyi Bao, Siyang Sun, Shuailei Ma, Kecheng Zheng, Yuxin Guo, Guosheng Zhao, Yun Zheng, Xingang Wang
We believe that the act of reasoning segmentation should mirror the cognitive stages of human visual search, where each step is a progressive refinement of thought toward the final object.
no code implementations • 11 Mar 2024 • Guosheng Zhao, XiaoFeng Wang, Zheng Zhu, Xinze Chen, Guan Huang, Xiaoyi Bao, Xingang Wang
DriveDreamer-2 is the first world model to generate customized driving videos, it can generate uncommon driving videos (e. g., vehicles abruptly cut in) in a user-friendly manner.
no code implementations • 18 Dec 2023 • Shuailei Ma, Chen-Wei Xie, Ying WEI, Siyang Sun, Jiaqi Fan, Xiaoyi Bao, Yuxin Guo, Yun Zheng
In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questions: $(i)$ How do the learned multi-modal prompts improve the recognition performance?
no code implementations • 11 Dec 2023 • Xiaoyi Bao, Jie Qin, Siyang Sun, Yun Zheng, Xingang Wang
To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences.
1 code implementation • 15 Jun 2023 • Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang, Yue Zhang, Guodong Zhou
To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure.
no code implementations • IJCAI 2022 • Xiaoyi Bao, Wang Zhongqing, Xiaotong Jiang, Rong Xiao, Shoushan Li
Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)
no code implementations • 29 Jun 2020 • Die Zhang, Huilin Zhou, Hao Zhang, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, Quanshi Zhang
This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing.