62 citations · 176 across the 28 of their papers we have counts for
10 papers · 1 filter
Cube: A Roblox View of 3D Intelligence
Foundation AI Team, Kiran Bhat, Nishchaie Khanna +44
Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Ro…
Learning Subject-Aware Cropping by Outpainting Professional Photos
James Hong, Lu Yuan, Michaël Gharbi +2
How to frame (or crop) a photo often depends on the image subject and its context; e.g., a human portrait. Recent works have defined the subject-aware image cropping task as a nuan…
Collage Diffusion
Vishnu Sarukkai, Linden Li, Arden Ma +2
We seek to give users precise control over diffusion-based image generation by modeling complex scenes as sequences of layers, which define the desired spatial arrangement and visu…
Spotting Temporally Precise, Fine-Grained Events in Video
James Hong, Haotian Zhang, Michaël Gharbi +2
We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason g…
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
Fait Poms, Vishnu Sarukkai, Ravi Teja Mullapudi +4
For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistic…
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
James Hong, Matthew Fisher, Michaël Gharbi +1
Human pose is a useful feature for fine-grained sports action understanding. However, pose estimators are often unreliable when run on sports video due to domain shift and factors…
Iterative Text-based Editing of Talking-heads Using Neural Retargeting
Xinwei Yao, Ohad Fried, Kayvon Fatahalian +1
We present a text-based tool for editing talking-head video that enables an iterative editing workflow. On each iteration users can edit the wording of the speech, further refine m…
Background Splitting: Finding Rare Classes in a Sea of Background
Ravi Teja Mullapudi, Fait Poms, William R. Mark +2
We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to t…
Online Model Distillation for Efficient Video Inference
Ravi Teja Mullapudi, Steven Chen, Keyi Zhang +2
High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fr…
Scanner: Efficient Video Analysis at Scale
Alex Poms, Will Crichton, Pat Hanrahan +1
A growing number of visual computing applications depend on the analysis of large video collections. The challenge is that scaling applications to operate on these datasets require…