Abstract When deploying the Unmanned Aerial Vehicles object detection (UAV-OD) network
to complex and unseen real-world scenarios, the generalization ability is usually reduced …

Abstract Domain generalization (DG) aims to learn a model that generalizes well to unseen
target domains utilizing multiple source domains without re-training. Most existing DG works …

Abstract Domain generalization (DG) is a principal task to evaluate the robustness of
computer vision models. Many previous studies have used normalization for DG. In …

Cross-domain image retrieval has been extensively studied due to its high practical value. In
recently proposed unsupervised cross-domain image retrieval methods, efforts are taken to …

In underwater perception and maritime surveys, due to the scarcity of training data and
perturbation of speckle noise, the detection performance of underwater objects in side-scan …

Synthetic data offers the promise of cheap and bountiful training data for settings where
labeled real-world data is scarce. However, models trained on synthetic data significantly …

Document recapturing is a presentation attack that covers the forensic traces in the digital
domain. Document presentation attack detection (DPAD) is an important step in the …

Few-shot class-incremental learning was introduced to solve the model adaptation problem
for new incremental classes with only a few examples while still remaining effective for old …

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a
source domain dataset where sufficient data is available, and then generalize models to …

Data augmentation is an effective approach to enhance generalization of object tracking
models, which has been widely applied into intelligent transportation to handle the …