37 citations · 76 across the 14 of their papers we have counts for
15 papers
Posterior Augmented Flow Matching
George Stoica, Sayak Paul, Matthew Wallingford +6
Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensional images, each train…
Seeing Fast and Slow: Learning the Flow of Time in Videos
Yen-Siang Wu, Rundong Luo, Jingsen Zhu +6
How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision researc…
Beyond the Frame: Generating 360 Panoramic Videos from Perspective Videos
Rundong Luo, Matthew Wallingford, Ali Farhadi +2
360° videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a m…
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,…
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better
Scott Geng, Cheng-Yu Hsieh, Vivek Ramanujan +4
Generative text-to-image models enable us to synthesize unlimited amounts of images in a controllable manner, spurring many recent efforts to train vision models with synthetic dat…
Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass
Ethan Shen, Alan Fan, Sarah M. Pratt +7
Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggest…
Multilingual Diversity Improves Vision-Language Representations
Thao Nguyen, Matthew Wallingford, Sebastin Santy +5
Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on s…
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…
Neural Priming for Sample-Efficient Adaptation
Matthew Wallingford, Vivek Ramanujan, Alex Fang +5
We propose Neural Priming, a technique for adapting large pretrained models to distribution shifts and downstream tasks given few or no labeled examples. Presented with class names…
Neural Radiance Field Codebooks
Matthew Wallingford, Aditya Kusupati, Alex Fang +4
Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representa…
Matryoshka Representation Learning
Aditya Kusupati, Gantavya Bhatt, Aniket Rege +8
Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computa…
Task Adaptive Parameter Sharing for Multi-Task Learning
Matthew Wallingford, Hao Li, Alessandro Achille +4
Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models…
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan +6
Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional ne…
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid +3
Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual…
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…