115 citations · 334 across the 15 of their papers we have counts for
9 papers · 1 filter
Diffusion Self-Guidance for Controllable Image Generation
Dave Epstein, Allan Jabri, Ben Poole +2
Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey…
DORSal: Diffusion for Object-centric Representations of Scenes et al
Allan Jabri, Sjoerd van Steenkiste, Emiel Hoogeboom +2
Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes an…
Object Permanence Emerges in a Random Walk along Memory
Pavel Tokmakov, Allan Jabri, Jie Li +1
This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence. A central question is th…
Discovering Objects that Can Move
Zhipeng Bao, Pavel Tokmakov, Allan Jabri +3
This paper studies the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, text…
Learning Pixel Trajectories with Multiscale Contrastive Random Walks
Zhangxing Bian, Allan Jabri, Alexei A. Efros +1
A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence. Yet, approaches that d…
Space-Time Correspondence as a Contrastive Random Walk
Allan Jabri, Andrew Owens, Alexei A. Efros
This paper proposes a simple self-supervised approach for learning a representation for visual correspondence from raw video. We cast correspondence as prediction of links in a spa…
Learning Correspondence from the Cycle-Consistency of Time
Xiaolong Wang, Allan Jabri, Alexei A. Efros
We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for lear…
Learning Visual N-Grams from Web Data
Ang Li, Allan Jabri, Armand Joulin +1
Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts. The traditional approach of annotating thousands…
Learning Visual Features from Large Weakly Supervised Data
Armand Joulin, Laurens van der Maaten, Allan Jabri +1
Convolutional networks trained on large supervised dataset produce visual features which form the basis for the state-of-the-art in many computer-vision problems. Further improveme…