9 citations · 53 across the 9 of their papers we have counts for
13 papers · 1 filter
UniTok: A Unified Tokenizer for Visual Generation and Understanding
Chuofan Ma, Yi Jiang, Junfeng Wu +5
Visual generative and understanding models typically rely on distinct tokenizers to process images, presenting a key challenge for unifying them within a single framework. Recent s…
Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
Jihan Yang, Shusheng Yang, Anjali W. Gupta +3
Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scal…
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
Shengbang Tong, Ellis Brown, Penghao Wu +11
We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. While stronger language models can enhance multimodal capabilities, the design…
Can 3D Vision-Language Models Truly Understand Natural Language?
Weipeng Deng, Jihan Yang, Runyu Ding +4
Rapid advancements in 3D vision-language (3D-VL) tasks have opened up new avenues for human interaction with embodied agents or robots using natural language. Despite this progress…
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
Runyu Ding, Jihan Yang, Chuhui Xue +3
Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset. This task is challenging because…
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
Jihan Yang, Runyu Ding, Weipeng Deng +2
We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely \textbf{RegionPLC}, for open-world 3D scene understanding, aiming to identify a…