Chun-Peng Chang — accepted AI-conference papers

2025

3D Spatial Understanding in MLLMs: Disambiguation and Evaluation

Chun-Peng Chang, Alain Pagani, Didier Stricker

ICRA 2025

Multimodal Large Language Models (MLLMs) have made significant progress in tasks such as image captioning and question answering. However, while these models can generate realistic captions, they often struggle with providing precise instructions, particularly when it comes to localizing and disambi

Cited by 3 Source ↗code ↗Scholar ↗

2024

MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding

Chun-Peng Chang, Shaoxiang Wang, Alain Pagani, Didier Stricker

CVPR 2024poster

3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object recognition and struggle in interpreting complex linguistic queries particularly with descriptions that involve multiple a…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗