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Semi-supervised adaptive pseudo-label feature learning for hyperspectral image classification in internet of things
Hyperspectral image (HSI) in Internet of Things (IoT) is a typical small sample data set,
which is difficult and costly to label samples manually. In the feature extraction, it is difficult to …
which is difficult and costly to label samples manually. In the feature extraction, it is difficult to …
Alignsam: Aligning segment anything model to open context via reinforcement learning
Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …
demonstrated its impressive generalization capabilities in open-world scenarios with the …
RankMatch: Exploring the better consistency regularization for semi-supervised semantic segmentation
The key lie in semi-supervised semantic segmentation is how to fully exploit substantial
unlabeled data to improve the model's generalization performance by resorting to …
unlabeled data to improve the model's generalization performance by resorting to …
Allspark: Reborn labeled features from unlabeled in transformer for semi-supervised semantic segmentation
Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden
of time-consuming pixel-level manual labeling which leverages limited labeled data along …
of time-consuming pixel-level manual labeling which leverages limited labeled data along …
SemiVL: semi-supervised semantic segmentation with vision-language guidance
L Hoyer, DJ Tan, MF Naeem, L Van Gool… - European Conference on …, 2024 - Springer
In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …
labeled images along with a large corpus of unlabeled images to reduce the high annotation …
Understanding negative proposals in generic few-shot object detection
Recently, Few-Shot Object Detection (FSOD) has received considerable research attention
as a strategy for reducing reliance on extensively labeled bounding boxes. However, current …
as a strategy for reducing reliance on extensively labeled bounding boxes. However, current …
Class Probability Space Regularization for semi-supervised semantic segmentation
Semantic segmentation achieves fine-grained scene parsing in any scenario, making it one
of the key research directions to facilitate the development of human visual attention …
of the key research directions to facilitate the development of human visual attention …
Pseudo Labeling Methods for Semi-Supervised Semantic Segmentation: A Review and Future Perspectives
Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …
applications in scene understanding, medical image analysis, and remote sensing. With the …
Unimatch v2: Pushing the limit of semi-supervised semantic segmentation
Semi-supervised semantic segmentation (SSS) aims at learning rich visual knowledge from
cheap unlabeled images to enhance semantic segmentation capability. Among recent …
cheap unlabeled images to enhance semantic segmentation capability. Among recent …
Revisiting and maximizing temporal knowledge in semi-supervised semantic segmentation
In semi-supervised semantic segmentation, the Mean Teacher-and co-training-based
approaches are employed to mitigate confirmation bias and coupling problems. However …
approaches are employed to mitigate confirmation bias and coupling problems. However …