3 citations · 4 across the 5 of their papers we have counts for
5 papers
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o
Dingning Liu, Cheng Wang, Peng Gao +4
Multimodal Large Language Models (MLLMs) exhibit impressive capabilities across a variety of tasks, especially when equipped with carefully designed visual prompts. However, existi…
Towards Efficient and Intelligent Laser Weeding: Method and Dataset for Weed Stem Detection
Dingning Liu, Jinzhe Li, Haoyang Su +5
Weed control is a critical challenge in modern agriculture, as weeds compete with crops for essential nutrient resources, significantly reducing crop yield and quality. Traditional…
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Le Zhuo, Ruoyi Du, Han Xiao +19
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and vi…
Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models
Dingning Liu, Xiaoshui Huang, Yuenan Hou +5
In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point clo…
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
Dingning Liu, Xiaomeng Dong, Renrui Zhang +5
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals…