6 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…
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…
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…
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,…