9 citations · 17 across the 6 of their papers we have counts for
6 papers
LA-Pose: Latent Action Pretraining Meets Pose Estimation
Zhengqing Wang, Saurabh Nair, Prajwal Chidananda +4
This paper revisits camera pose estimation through the lens of self-supervised pretraining, focusing on inverse-dynamics pretraining as a scalable alternative to the current trend…
Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction
Samuel Li, Pujith Kachana, Prajwal Chidananda +3
Estimating agent pose and 3D scene structure from multi-camera rigs is a central task in embodied AI applications such as autonomous driving. Recent learned approaches such as DUSt…
OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects
Akshay Krishnan, Abhijit Kundu, Kevis-Kokitsi Maninis +2
We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box annotations for indoor and outdoor sc…
Module-wise Adaptive Distillation for Multimodality Foundation Models
Chen Liang, Jiahui Yu, Ming-Hsuan Yang +5
Pre-trained multimodal foundation models have demonstrated remarkable generalizability but pose challenges for deployment due to their large sizes. One effective approach to reduci…
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text
Qing Li, Boqing Gong, Yin Cui +4
In this paper, we explore the possibility of building a unified foundation model that can be adapted to both vision-only and text-only tasks. Starting from BERT and ViT, we design…
Exploring Temporal Granularity in Self-Supervised Video Representation Learning
Rui Qian, Yeqing Li, Liangzhe Yuan +7
This work presents a self-supervised learning framework named TeG to explore Temporal Granularity in learning video representations. In TeG, we sample a long clip from a video and…