3 accepted papers
Despite being trained on balanced datasets, existing AI-generated image detectors often exhibit systematic bias at test time, frequently misclassifying fake images as real. We hypothesize that this behavior stems from distributional shift in fake samples and implicit priors learned during training.
Despite being trained on massive data, today's vision foundation models still fall short in detecting open world objects. Apart from recognizing known objects from training, a successful Open World Object Detection (OWOD) system must also be able to detect unknown objects never seen before, without…
Most existing remote sensing instance segmentation approaches are designed for close-vocabulary prediction, limiting their ability to recognize novel categories or generalize across datasets. This restricts their applicability in diverse Earth observation scenarios. To address this, we introduce ope…