no code implementations • 10 May 2024 • Yifan Yu, Shaokun Wang, Yuhang He, Junzhe Chen, Yihong Gong
Continual Novel Class Discovery (CNCD) aims to continually discover novel classes without labels while maintaining the recognition capability for previously learned classes.
no code implementations • 27 Feb 2024 • Junzhe Chen, Qiao Yang, Senmao Tian, Shunli Zhang
It is critical to deploy complicated neural network models on hardware with limited resources.
no code implementations • 26 Feb 2024 • Junzhe Chen, Xuming Hu, Shuodi Liu, Shiyu Huang, Wei-Wei Tu, Zhaofeng He, Lijie Wen
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence.
no code implementations • 25 Feb 2024 • Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, Zhijiang Guo
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
1 code implementation • 25 Oct 2023 • Xuming Hu, Junzhe Chen, Aiwei Liu, Shiao Meng, Lijie Wen, Philip S. Yu
Additionally, our method is orthogonal to previous multimodal fusions, and using it on prior SOTA fusions further improves 5. 47% F1.
no code implementations • 8 Oct 2023 • Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, Zhijiang Guo
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
no code implementations • 26 Sep 2023 • Qiao Yang, Yu Zhang, Jian Zhang, Zijing Zhao, Shunli Zhang, Jinqiao Wang, Junzhe Chen
Most existing learning-based infrared and visible image fusion (IVIF) methods exhibit massive redundant information in the fusion images, i. e., yielding edge-blurring effect or unrecognizable for object detectors.
no code implementations • 25 May 2023 • Xuming Hu, Junzhe Chen, Zhijiang Guo, Philip S. Yu
Evidence plays a crucial role in automated fact-checking.