9 citations · 20 across the 5 of their papers we have counts for
10 papers
ActiveNeRF: Learning where to See with Uncertainty Estimation
Xuran Pan, Zihang Lai, Shiji Song +1
Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective,…
Learning to Weight Samples for Dynamic Early-exiting Networks
Yizeng Han, Yifan Pu, Zihang Lai +6
Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks…
Domain Adaptation via Prompt Learning
Chunjiang Ge, Rui Huang, Mixue Xie +4
Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approach…
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion
Zihang Lai, Sifei Liu, Alexei A. Efros +1
A video autoencoder is proposed for learning disentan- gled representations of 3D structure and camera pose from videos in a self-supervised manner. Relying on temporal continuity…
The Functional Correspondence Problem
Zihang Lai, Senthil Purushwalkam, Abhinav Gupta
The ability to find correspondences in visual data is the essence of most computer vision tasks. But what are the right correspondences? The task of visual correspondence is well d…
MAST: A Memory-Augmented Self-supervised Tracker
Zihang Lai, Erika Lu, Weidi Xie
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained o…