6 papers
Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models
Yifei Peng, Yaoli Liu, Enbo Xia +5
We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves bot…
ROVI: A VLM-LLM Re-Captioned Dataset for Open-Vocabulary Instance-Grounded Text-to-Image Generation
Cihang Peng, Qiming Hou, Zhong Ren +1
We present ROVI, a high-quality synthetic dataset for instance-grounded text-to-image generation, created by labeling 1M curated web images. Our key innovation is a strategy called…
Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation
Jingming Liu, Yumeng Li, Wei Shi +3
Recent studies have proposed leveraging Large Language Models (LLMs) as information retrievers through query rewriting. However, for challenging corpora, we argue that enhancing qu…
Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning
Yu Jin, Jingming Liu, Zhexu Luo +5
Visual generative abductive learning studies jointly training symbol-grounded neural visual generator and inducing logic rules from data, such that after learning, the visual gener…
Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models
Jingming Liu, Yumeng Li, Boyuan Xiao +5
Under pure textual modality, Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning tasks by decomposing them into simpler sub-problems. However, Mu…
Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation
Bohong Chen, Yumeng Li, Yao-Xiang Ding +2
Current co-speech motion generation approaches usually focus on upper body gestures following speech contents only, while lacking supporting the elaborate control of synergistic fu…