4 accepted papers
Deploying semantic segmentation in driving and robotics requires both real-time inference and robustness to domain shifts, formalized as Real-Time Domain-Generalized Semantic Segmentation (RT-DGSS), which has not been fully addressed. Existing methods often treat real-time(RT) inference and domain g…
Recent advances in Open Vocabulary Object Detection (OVOD) have shown strong performance on standard benchmarks, but performance drops sharply under out-of-distribution (OOD) shifts. Continual learning offers a potential remedy by sequentially integrating new tasks, yet existing methods often strugg…
Existing Incremental Learning (IL) methods are primarily evaluated under either a single-domain class-incremental setting, or a multi-domain task-incremental setting with known task identifiers. However, these assumptions often fail to hold in real-world applications. To bridge this gap, we introduc
Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to generalize to unseen domains by leveraging a few annotated samples of the target domain, requiring models to exhibit both strong generalization and localization capabilities. However, existing well-trained detectors typically have strong loca…