71 citations · 93 across the 15 of their papers we have counts for
27 papers
Tracking by Predicting 3-D Gaussians Over Time
Tanish Baranwal, Himanshu Gaurav Singh, Jathushan Rajasegaran +1
We propose Video Gaussian Masked Autoencoders (Video-GMAE), a self-supervised approach for representation learning that encodes a sequence of images into a set of Gaussian splats m…
Perception Encoder: The best visual embeddings are not at the output of the network
Daniel Bolya, Po-Yao Huang, Peize Sun +15
We introduce Perception Encoder (PE), a state-of-the-art vision encoder for image and video understanding trained via simple vision-language learning. Traditionally, vision encoder…
Poly-Autoregressive Prediction for Modeling Interactions
Neerja Thakkar, Tara Sadjadpour, Jathushan Rajasegaran +2
We introduce a simple framework for predicting the behavior of an agent in multi-agent settings. In contrast to autoregressive (AR) tasks, such as language processing, our focus is…
An Empirical Study of Autoregressive Pre-training from Videos
Jathushan Rajasegaran, Ilija Radosavovic, Rahul Ravishankar +3
We empirically study autoregressive pre-training from videos. To perform our study, we construct a series of autoregressive video models, called Toto. We treat videos as sequences…
Gaussian Masked Autoencoders
Jathushan Rajasegaran, Xinlei Chen, Rulilong Li +3
This paper explores Masked Autoencoders (MAE) with Gaussian Splatting. While reconstructive self-supervised learning frameworks such as MAE learns good semantic abstractions, it is…
Scaling Properties of Diffusion Models for Perceptual Tasks
Rahul Ravishankar, Zeeshan Patel, Jathushan Rajasegaran +1
In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as…