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Semmae: Semantic-guided masking for learning masked autoencoders
Recently, significant progress has been made in masked image modeling to catch up to
masked language modeling. However, unlike words in NLP, the lack of semantic …
masked language modeling. However, unlike words in NLP, the lack of semantic …
Leaving reality to imagination: Robust classification via generated datasets
Recent research on robustness has revealed significant performance gaps between neural
image classifiers trained on datasets that are similar to the test set, and those that are from a …
image classifiers trained on datasets that are similar to the test set, and those that are from a …
Mask-guided correlation learning for few-shot segmentation in remote sensing imagery
Few-shot segmentation aims to segment specific objects in a query image based on a few
densely annotated images and has been extensively studied in recent years. In remote …
densely annotated images and has been extensively studied in recent years. In remote …
Adapt before comparison: A new perspective on cross-domain few-shot segmentation
J Herzog - Proceedings of the IEEE/CVF conference on …, 2024 - openaccess.thecvf.com
Few-shot segmentation performance declines substantially when facing images from a
domain different than the training domain effectively limiting real-world use cases. To …
domain different than the training domain effectively limiting real-world use cases. To …
Latency-free driving scene prediction for on-road teledriving with future-image-generation
Teledriving could serve as a practical solution for handling unforeseen situations in
autonomous driving. However, the latency of transmission networks remains a prominent …
autonomous driving. However, the latency of transmission networks remains a prominent …
Adversarial example detection using semantic graph matching
Deep neural networks have recently been found to be vulnerable to adversarial examples,
which can deceive attacked models with high confidence. This has given rise to significant …
which can deceive attacked models with high confidence. This has given rise to significant …
Improving few-shot part segmentation using coarse supervision
A significant bottleneck in training deep networks for part segmentation is the cost of
obtaining detailed annotations. We propose a framework to exploit coarse labels such as …
obtaining detailed annotations. We propose a framework to exploit coarse labels such as …
ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning
Zero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is
typically achieved by generating visual features for unseen classes based on learned visual …
typically achieved by generating visual features for unseen classes based on learned visual …
PartSeg: Few-shot part segmentation via part-aware prompt learning
In this work, we address the task of few-shot part segmentation, which aims to segment the
different parts of an unseen object using very few labeled examples. It has been found that …
different parts of an unseen object using very few labeled examples. It has been found that …
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges
Semantic segmentation, vital for applications ranging from autonomous driving to robotics,
faces significant challenges in domains where collecting large annotated datasets is difficult …
faces significant challenges in domains where collecting large annotated datasets is difficult …