312 citations · 457 across the 9 of their papers we have counts for
11 papers
OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Huong Ngo, Matt Deitke, Martijn Bartelds +4
Improvements in training data scale and quality have led to significant advances, yet its influence in speech recognition remains underexplored. In this paper, we present a large-s…
From an Image to a Scene: Learning to Imagine the World from a Million 360 Videos
Matthew Wallingford, Anand Bhattad, Aditya Kusupati +7
Three-dimensional (3D) understanding of objects and scenes play a key role in humans' ability to interact with the world and has been an active area of research in computer vision,…
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Matt Deitke, Christopher Clark, Sangho Lee +47
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good perfor…
Objaverse-XL: A Universe of 10M+ 3D Objects
Matt Deitke, Ruoshi Liu, Matthew Wallingford +14
Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data. However, 3D vision tasks hav…
Objaverse: A Universe of Annotated 3D Objects
Matt Deitke, Dustin Schwenk, Jordi Salvador +7
Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datase…
Phone2Proc: Bringing Robust Robots Into Our Chaotic World
Matt Deitke, Rose Hendrix, Luca Weihs +3
Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their in…
Retrospectives on the Embodied AI Workshop
Matt Deitke, Dhruv Batra, Yonatan Bisk +36
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped i…
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Matt Deitke, Eli VanderBilt, Alvaro Herrasti +8
Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable simila…
Visual Room Rearrangement
Luca Weihs, Matt Deitke, Aniruddha Kembhavi +1
There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within…
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
Matt Deitke, Winson Han, Alvaro Herrasti +10
Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embod…
AI2-THOR: An Interactive 3D Environment for Visual AI
Eric Kolve, Roozbeh Mottaghi, Winson Han +10
We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at http://ai2thor.allenai.org. AI2-THOR consists of near photo-realistic 3D indoor scen…