no code implementations • 23 Dec 2023 • Jiaxin Ge, Xinyan Chen, Tianjun Zhang, Shanghang Zhang
IP-RLDF first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback.
no code implementations • 4 Dec 2023 • Jiaxin Ge, Sanjay Subramanian, Baifeng Shi, Roei Herzig, Trevor Darrell
Visual Programming (VP) has emerged as a powerful framework for Visual Question Answering (VQA).
no code implementations • 21 Nov 2023 • Jiaxin Ge, Sanjay Subramanian, Trevor Darrell, Boyi Li
Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a $\textbf{Re}$cursive $\textbf{Vis}$ual $\textbf{E}$xplanation algorithm.
no code implementations • 3 Oct 2023 • Zhao Kaiya, Michelangelo Naim, Jovana Kondic, Manuel Cortes, Jiaxin Ge, Shuying Luo, Guangyu Robert Yang, Andrew Ahn
Meanwhile, our techniques enabled Lyfe Agents to operate at a computational cost 10-100 times lower than existing alternatives.
1 code implementation • 19 Sep 2023 • Tianhua Zhang, Jiaxin Ge, Hongyin Luo, Yung-Sung Chuang, Mingye Gao, Yuan Gong, Xixin Wu, Yoon Kim, Helen Meng, James Glass
How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning?
1 code implementation • 26 May 2023 • Jiaxin Ge, Hongyin Luo, Yoon Kim, James Glass
Experiments on binary and multi-class classification tasks show that SimPLE leads to more robust self-training results, indicating that the self-trained entailment models are more efficient and trustworthy than large language models on language understanding tasks.
Multi-class Classification Natural Language Understanding +1
no code implementations • 16 Apr 2023 • Jiaxin Ge, Hongyin Luo, Siyuan Qian, Yulu Gan, Jie Fu, Shanghang Zhang
Chain of Thought is a simple and effective approximation to human reasoning process and has been proven useful for natural language processing (NLP) tasks.