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Found 8 papers, 5 papers with code
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
We then propose an effective and realistic attack method that better produces poisoned samples without access to benign samples, and derive an effective in-distribution attack objective.
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
In brief, we use the prompts of overlapping masks as corresponding negative signals, resulting in refined masks.
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
Specifically, we design a Test-Time Learning (TTL) module that employs a mean-teacher network for self-training to discover novel instances from test data, allowing detectors to learn better representations and classifiers for novel classes.
SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation
In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training.
CLIP-Guided Source-Free Object Detection in Aerial Images
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
Toward distortion-aware change detection in realistic scenarios
The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning.
Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes found in the unlabeled dataset are also represented in the labeled data.
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Object detection in remote sensing images relies on a large amount of labeled data for training.
