Dual-model Collaboration Consistency Semi-Supervised Learning for Few-shot Lithology Interpretation
Remote sensing of the geological environment (GERS) interpretation contributes to
lithological mapping, disaster prediction, soil erosion monitoring, etc. However, the rich …
lithological mapping, disaster prediction, soil erosion monitoring, etc. However, the rich …
Updating road maps at city scale with remote sensed images and existing vector maps
X Chen, A Yu, Q Sun, W Guo, Q Xu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Currently, many countries have built geoinformation databases and gathered large amounts
of geographic data. However, with the extensive construction of infrastructure and rapid …
of geographic data. However, with the extensive construction of infrastructure and rapid …
Enhancing the utilization of uncertain pixels in semi-supervised semantic segmentation
In semi-supervised semantic segmentation, determining the correct label for uncertain pixels
is crucial yet challenging. The recently proposed virtual category (VC) learning achieves …
is crucial yet challenging. The recently proposed virtual category (VC) learning achieves …
CoNPL: Consistency training framework with noise-aware pseudo labeling for dense pose estimation
Dense pose estimation faces hurdles due to the lack of costly precise pixel-level IUV labels.
Existing methods aim to overcome it by regularizing model outputs or interpolating pseudo …
Existing methods aim to overcome it by regularizing model outputs or interpolating pseudo …
AMED: Automatic Mixed-Precision Quantization for Edge Devices
Quantized neural networks are well known for reducing the latency, power consumption,
and model size without significant harm to the performance. This makes them highly …
and model size without significant harm to the performance. This makes them highly …
A Multi-View Integrated Ensemble for the Background Discrimination of Semi-Supervised Semantic Segmentation
H Gwak, Y Jeong, C Kim, Y Lee, S Yang, S Kim - Applied Sciences, 2023 - mdpi.com
The key to semi-supervised semantic segmentation is to assign the appropriate pseudo-
label to the pixels of unlabeled images. Recently, various approaches to consistency-based …
label to the pixels of unlabeled images. Recently, various approaches to consistency-based …
Boundary-refined prototype generation: A general end-to-end paradigm for semi-supervised semantic segmentation
Semi-supervised semantic segmentation has attracted increasing attention in computer
vision, aiming to leverage unlabeled data through latent supervision. To achieve this goal …
vision, aiming to leverage unlabeled data through latent supervision. To achieve this goal …