6 citations · 16 across the 10 of their papers we have counts for
10 papers
Test-Time Training on Video Streams
Renhao Wang, Yu Sun, Arnuv Tandon +4
Prior work has established Test-Time Training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction on each test instance, the mo…
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Renhao Wang, Jiayuan Mao, Joy Hsu +3
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent w…
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Yingdong Hu, Renhao Wang, Li Erran Li +1
In recent years, increasing attention has been directed to leveraging pre-trained vision models for motor control. While existing works mainly emphasize the importance of this pre-…
Robust and Controllable Object-Centric Learning through Energy-based Models
Ruixiang Zhang, Tong Che, Boris Ivanovic +4
Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a…
Semantic-Aware Fine-Grained Correspondence
Yingdong Hu, Renhao Wang, Kaifeng Zhang +1
Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representati…
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation
Renhao Wang, Hang Zhao, Yang Gao
Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap…