6 papers
Progressive Supernet Training for Efficient Visual Autoregressive Modeling
Xiaoyue Chen, Yuling Shi, Kaiyuan Li +5
Visual Auto-Regressive (VAR) models significantly reduce inference steps through the "next-scale" prediction paradigm. However, progressive multi-scale generation incurs substantia…
EgoPrune: Efficient Token Pruning for Egomotion Video Reasoning in Embodied Agent
Jiaao Li, Kaiyuan Li, Chen Gao +2
Egomotion videos are first-person recordings where the view changes continuously due to the agent's movement. As they serve as the primary visual input for embodied AI agents, maki…
AirScape: An Aerial Generative World Model with Motion Controllability
Baining Zhao, Rongze Tang, Mingyuan Jia +9
How to enable agents to predict the outcomes of their own motion intentions in three-dimensional space has been a fundamental problem in embodied intelligence. To explore general s…
Balanced Token Pruning: Accelerating Vision Language Models Beyond Local Optimization
Kaiyuan Li, Xiaoyue Chen, Chen Gao +2
Large Vision-Language Models (LVLMs) have shown impressive performance across multi-modal tasks by encoding images into thousands of tokens. However, the large number of image toke…
PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large Vision-Language Models
Yu Meng, Kaiyuan Li, Chenran Huang +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a range of multimodal tasks. However, their inference efficiency is constrained by the large n…
Understanding and Evaluating Hallucinations in 3D Visual Language Models
Ruiying Peng, Kaiyuan Li, Weichen Zhang +3
Recently, 3D-LLMs, which combine point-cloud encoders with large models, have been proposed to tackle complex tasks in embodied intelligence and scene understanding. In addition to…