2 citations · 4 across the 8 of their papers we have counts for
8 papers
SAB3R: Semantic-Augmented Backbone in 3D Reconstruction
Xuweiyi Chen, Tian Xia, Sihan Xu +3
We introduce a new task, Map and Locate, which unifies the traditionally distinct objectives of open-vocabulary segmentation - detecting and segmenting object instances based on na…
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Jianing Yang, Alexander Sax, Kevin J. Liang +6
Multi-view 3D reconstruction remains a core challenge in computer vision, particularly in applications requiring accurate and scalable representations across diverse perspectives.…
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use
Jiajun Xi, Yinong He, Jianing Yang +2
In real-world scenarios, it is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks. Despite recen…
Multi-Object Hallucination in Vision-Language Models
Xuweiyi Chen, Ziqiao Ma, Xuejun Zhang +5
Large vision language models (LVLMs) often suffer from object hallucination, producing objects not present in the given images. While current benchmarks for object hallucination pr…
LinkGPT: Teaching Large Language Models To Predict Missing Links
Zhongmou He, Jing Zhu, Shengyi Qian +2
Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, par…
3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination
Jianing Yang, Xuweiyi Chen, Nikhil Madaan +4
The integration of language and 3D perception is crucial for embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) hav…
GROUNDHOG: Grounding Large Language Models to Holistic Segmentation
Yichi Zhang, Ziqiao Ma, Xiaofeng Gao +3
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences…
LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent
Jianing Yang, Xuweiyi Chen, Shengyi Qian +4
3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approac…