Search Results for author:
Found 7 papers, 5 papers with code
ImageBind: One Embedding Space To Bind Them All
We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together.
Ranked #2 on
Zero-shot Classification (unified classes)
on LLVIP
The effectiveness of MAE pre-pretraining for billion-scale pretraining
While MAE has only been shown to scale with the size of models, we find that it scales with the size of the training dataset as well.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 10-shot
(using extra training data)
OmniMAE: Single Model Masked Pretraining on Images and Videos
Furthermore, this model can be learned by dropping 90% of the image and 95% of the video patches, enabling extremely fast training of huge model architectures.
Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction
Our final 3D reconstruction model is also capable of zero-shot inference on images from unseen object categories and we empirically show that increasing the number of training categories improves the reconstruction quality.
PyTorchVideo: A Deep Learning Library for Video Understanding
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing.
Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation
We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation.
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
This paper introduces PyRobot, an open-source robotics framework for research and benchmarking.
