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Found 8 papers, 6 papers with code
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detection
Specifically, change missing refer to the situation that the WSCD model fails to predict any changed pixels, even though the image-level label indicates changed, and vice versa for change fabricating.
Token Contrast for Weakly-Supervised Semantic Segmentation
Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers
Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers.
Ranked #28 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.
Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings.
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection.
