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Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

17 March 2023
Senqiao Yang
Jiarui Wu
Jiaming Liu
Xiaoqi Li
Qizhe Zhang
Mingjie Pan
Yulu Gan
Zehui Chen
Shanghang Zhang
    MDE
    VLM
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Papers citing "Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction"

7 / 7 papers shown
Title
FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation
FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation
Dong Zhao
Jinlong Li
Shuang Wang
Mengyao Wu
Qi Zang
N. Sebe
Zhun Zhong
60
0
0
23 Mar 2025
Real-World Robot Learning with Masked Visual Pre-training
Real-World Robot Learning with Masked Visual Pre-training
Ilija Radosavovic
Tete Xiao
Stephen James
Pieter Abbeel
Jitendra Malik
Trevor Darrell
SSL
146
238
0
06 Oct 2022
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
Haoran Chen
Xintong Han
Zuxuan Wu
Yu-Gang Jiang
77
25
0
30 Sep 2022
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime
  Semantic Segmentation
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
Xinyi Wu
Zhenyao Wu
Haojie Guo
L. Ju
Song Wang
97
143
0
22 Apr 2021
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
Dayan Guan
Jiaxing Huang
Aoran Xiao
Shijian Lu
Yanpeng Cao
171
111
0
27 Feb 2021
Deep Ordinal Regression Network for Monocular Depth Estimation
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu
Mingming Gong
Chaohui Wang
Kayhan Batmanghelich
Dacheng Tao
MDE
180
1,687
0
06 Jun 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
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