no code implementations • 13 Apr 2024 • Chenming Shang, Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, Yuwang Wang
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process.
no code implementations • 1 Apr 2024 • Xinzhe Ni, Yeyun Gong, Zhibin Gou, Yelong Shen, Yujiu Yang, Nan Duan, Weizhu Chen
Additionally, we showcase the use of QaDS in creating efficient fine-tuning mixtures with various selection ratios, and analyze the quality of a wide range of open-source datasets, which can perform as a reference for future works on mathematical reasoning tasks.
no code implementations • 13 Nov 2023 • Xuejing Liu, Wei Tang, Xinzhe Ni, Jinghui Lu, Rui Zhao, Zechao Li, Fei Tan
This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets.
no code implementations • 3 Nov 2023 • Yifan Wang, Qingyan Guo, Xinzhe Ni, Chufan Shi, Lemao Liu, Haiyun Jiang, Yujiu Yang
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform well on downstream tasks.
no code implementations • 9 Dec 2022 • Xinzhe Ni, Yong liu, Hao Wen, Yatai Ji, Jing Xiao, Yujiu Yang
Then in the visual flow, visual prototypes are computed by a Temporal-Relational CrossTransformer (TRX) module for example.