Search Results for author:
Found 18 papers, 7 papers with code
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Siamese Network with Interactive Transformer for Video Object Segmentation
Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied in recent years.
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
Our principal insight is that the depth can be harmonized with the panoptic segmentation with our proposed new paradigm of predicting instance level depth maps with object queries.
Monocular Road Planar Parallax Estimation
By warping the consecutive frames using the road plane as a reference, the 3D structure can be estimated from the planar parallax and the residual image displacements.
Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
In this paper, we propose a novel stagewise domain adaptation model called RoadDA to address the DS issue in this field.
DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
In addition, feature-level alignment is carried out by aligning the feature maps of the source and target images from student network using a weighted maximum mean discrepancy loss.
Ranked #11 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery
Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.
LocalDrop: A Hybrid Regularization for Deep Neural Networks
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.
Recurrent Feature Reasoning for Image Inpainting
To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR.
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.
Exploring Representativeness and Informativeness for Active Learning
Then can we find a way to fuse the two active sampling criteria without any assumption on data?
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Meanwhile, it is also hard to build a good model without diagnosing discriminative labels.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
Defect Detection from UAV Images based on Region-Based CNNs
In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.
Ranked #14 on
Unsupervised Domain Adaptation
on Market to Duke
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested information and knowledge from remote sensing (RS) images with high efficiency and accuracy.
