Search Results for author: Zihang Lai

Found 12 papers, 9 papers with code

ActiveNeRF: Learning where to See with Uncertainty Estimation

1 code implementation18 Sep 2022 Xuran Pan, Zihang Lai, Shiji Song, Gao Huang

In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget.

Active Learning Novel View Synthesis

Learning to Weight Samples for Dynamic Early-exiting Networks

1 code implementation17 Sep 2022 Yizeng Han, Yifan Pu, Zihang Lai, Chaofei Wang, Shiji Song, Junfen Cao, Wenhui Huang, Chao Deng, Gao Huang

Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers.


Extreme Masking for Learning Instance and Distributed Visual Representations

1 code implementation9 Jun 2022 Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin

The paper presents a scalable approach for learning spatially distributed visual representations over individual tokens and a holistic instance representation simultaneously.

Data Augmentation Representation Learning

Domain Adaptation via Prompt Learning

1 code implementation14 Feb 2022 Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, Gao Huang

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.

Domain Adaptation

AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

1 code implementation CVPR 2022 Yulin Wang, Yang Yue, Yuanze Lin, Haojun Jiang, Zihang Lai, Victor Kulikov, Nikita Orlov, Humphrey Shi, Gao Huang

Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.

Video Recognition

The Functional Correspondence Problem

no code implementations ICCV 2021 Zihang Lai, Senthil Purushwalkam, Abhinav Gupta

For example, what are the correspondences between a bottle and shoe for the task of pounding or the task of pouring.

MAST: A Memory-Augmented Self-supervised Tracker

2 code implementations CVPR 2020 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.

Semantic Segmentation Semi-Supervised Video Object Segmentation +2

Self-supervised Learning for Video Correspondence Flow

1 code implementation2 May 2019 Zihang Lai, Weidi Xie

Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation.

Self-Supervised Learning Semi-Supervised Video Object Segmentation +4

Anytime Stereo Image Depth Estimation on Mobile Devices

2 code implementations26 Oct 2018 Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger

Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.

Stereo Depth Estimation

Neural Allocentric Intuitive Physics Prediction from Real Videos

no code implementations7 Sep 2018 Zhihua Wang, Stefano Rosa, Yishu Miao, Zihang Lai, Linhai Xie, Andrew Markham, Niki Trigoni

In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.

End-to-End Refinement Guided by Pre-trained Prototypical Classifier

1 code implementation7 May 2018 Junwen Bai, Zihang Lai, Runzhe Yang, Yexiang Xue, John Gregoire, Carla Gomes

We propose imitation refinement, a novel approach to refine imperfect input patterns, guided by a pre-trained classifier incorporating prior knowledge from simulated theoretical data, such that the refined patterns imitate the ideal data.

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