56 citations · 99 across the 11 of their papers we have counts for
12 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…
Extreme Masking for Learning Instance and Distributed Visual Representations
Zhirong Wu, Zihang Lai, Xiao Sun +1
The paper presents a scalable approach for learning spatially distributed visual representations over individual tokens and a holistic instance representation simultaneously. We us…
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…
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Yulin Wang, Yang Yue, Yuanze Lin +6
Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy. As a representative work, the adapt…
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…
Self-supervised Learning for Video Correspondence Flow
Zihang Lai, Weidi Xie
The objective of this paper is self-supervised learning of feature embeddings that are suitable for matching correspondences along the videos, which we term correspondence flow. By…
Anytime Stereo Image Depth Estimation on Mobile Devices
Yan Wang, Zihang Lai, Gao Huang +4
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-o…
Neural Allocentric Intuitive Physics Prediction from Real Videos
Zhihua Wang, Stefano Rosa, Yishu Miao +4
Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong ass…
End-to-End Refinement Guided by Pre-trained Prototypical Classifier
Junwen Bai, Zihang Lai, Runzhe Yang +3
Many real-world tasks involve identifying patterns from data satisfying background or prior knowledge. In domains like materials discovery, due to the flaws and biases in raw exper…