5 citations · 7 across the 8 of their papers we have counts for
8 papers
A Mixed Diet Makes DINO An Omnivorous Vision Encoder
Rishabh Kabra, Maks Ovsjanikov, Drew A. Hudson +5
Pre-trained vision encoders like DINOv2 have demonstrated exceptional performance on unimodal tasks. However, we observe that their features are poorly aligned across different vis…
Recurrent Video Masked Autoencoders
Daniel Zoran, Nikhil Parthasarathy, Yi Yang +3
We present Recurrent Video Masked-Autoencoders (RVM): a novel approach to video representation learning that leverages recurrent computation to model the temporal structure of vide…
LayerLock: Non-collapsing Representation Learning with Progressive Freezing
Goker Erdogan, Nikhil Parthasarathy, Catalin Ionescu +5
We introduce LayerLock, a simple yet effective approach for self-supervised visual representation learning, that gradually transitions from pixel to latent prediction through progr…
Scaling 4D Representations
João Carreira, Dilara Gokay, Michael King +32
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations on semantic-related tasks $\unicode{x20…
How to Spin an Object: First, Get the Shape Right
Rishabh Kabra, Drew A. Hudson, Sjoerd van Steenkiste +2
Image-to-3D models increasingly rely on hierarchical generation to disentangle geometry and texture. However, the design choices underlying these two-stage models--particularly the…
Moving Off-the-Grid: Scene-Grounded Video Representations
Sjoerd van Steenkiste, Daniel Zoran, Yi Yang +13
Current vision models typically maintain a fixed correspondence between their representation structure and image space. Each layer comprises a set of tokens arranged "on-the-grid,"…
Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models
Ziyi Wu, Yulia Rubanova, Rishabh Kabra +6
We address the problem of multi-object 3D pose control in image diffusion models. Instead of conditioning on a sequence of text tokens, we propose to use a set of per-object repres…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…