1 citations · 1 across the 9 of their papers we have counts for
10 papers
Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
Boyuan Xiao, Bohong Chen, Yumeng Li +3
In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs & VLAs) are powerful tools wi…
FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time
Yaoli Liu, Yao-Xiang Ding, Kun Zhou
This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies…
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…
QPoser: Quantized Explicit Pose Prior Modeling for Controllable Pose Generation
Yumeng Li, Yaoxiang Ding, Zhong Ren +1
Explicit pose prior models compress human poses into latent representations for using in pose-related downstream tasks. A desirable explicit pose prior model should satisfy three d…
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Yifei Peng, Zijie Zha, Yu Jin +5
Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abducti…