Search Results for author:
Found 11 papers, 5 papers with code
Unsupervised CD in satellite image time series by contrastive learning and feature tracking
In this work, we propose a two-stage approach to unsupervised change detection in satellite image time-series using contrastive learning with feature tracking.
Incomplete Multimodal Learning for Remote Sensing Data Fusion
The mechanism of connecting multimodal signals through self-attention operation is a key factor in the success of multimodal Transformer networks in remote sensing data fusion tasks.
VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation
The proposed VTP framework integrates the high performance of the transformer with volumetric representations, which can be used as a good alternative to the convolutional backbones.
Ranked #4 on
3D Human Pose Estimation
on Panoptic
(using extra training data)
Exploring Global Diversity and Local Context for Video Summarization
By combining these two attention mechanisms, a video SUMmarization model with Diversified Contextual Attention scheme is developed, namely SUM-DCA.
Self-supervised Remote Sensing Images Change Detection at Pixel-level
To overcome the effects of regular seasonal changes in binary change maps, we also used an uncertainty method to enhance the temporal robustness of the proposed approach.
Self-supervised Change Detection in Multi-view Remote Sensing Images
In this approach, a pseudo-Siamese network is trained to regress the output between its two branches pre-trained in a contrastive way on a large dataset of multi-temporal homogeneous or heterogeneous image patches.
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images
For the land-cover mapping task, we assign each pixel a land-cover class by the joint use of pre-trained features and spectral information of the image itself.
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images.
Harnessing the linguistic signal to predict scalar inferences
Pragmatic inferences often subtly depend on the presence or absence of linguistic features.
DialectGram: Detecting Dialectal Variation at Multiple Geographic Resolutions
In this work, we propose a model that enables detection of dialectal variation at multiple levels of geographic resolution obviating the need for a-priori definition of the resolution level.
