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20172026
most citedA Billion-scale Foundation Model for Remote Sensing Images

81 citations · 184 across the 17 of their papers we have counts for

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8 papers · 1 filter

cs.CV2023★ 5 cited

Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer

Hakjin Lee, MinKi Song, Jamyoung Koo +1

Detection Transformers (DETR) have recently set new benchmarks in object detection. However, their performance in detecting rotated objects lags behind established oriented object…

cs.CV2023★ 81 cited

A Billion-scale Foundation Model for Remote Sensing Images

Keumgang Cha, Junghoon Seo, Taekyung Lee

As the potential of foundation models in visual tasks has garnered significant attention, pretraining these models before downstream tasks has become a crucial step. The three key…

cs.CV2023★ 6 cited

Implicit Stacked Autoregressive Model for Video Prediction

Minseok Seo, Hakjin Lee, Doyi Kim +1

Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive. Autoregressive methods rely on the Markov assumption and can achieve…

cs.CV2022

Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery

Minseok Seo, Hakjin Lee, Yongjin Jeon +1

For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision. To overcome this i…

cs.CV2021★ 26 cited

Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation

Keumgang Cha, Junghoon Seo, Yeji Choi

In the training of deep learning models, how the model parameters are initialized greatly affects the model performance, sample efficiency, and convergence speed. Representation le…

cs.CV2021

Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

Chaehyeon Lee, Junghoon Seo, Heechul Jung

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because t…