3 accepted papers
Deep learning-based image manipulation localization (IML) methods have achieved remarkable performance in recent years, but typically rely on large-scale pixel-level annotated datasets. To address the challenge of acquiring high-quality annotations, some recent weakly supervised methods utilize imag
Image manipulation localization (IML) faces a fundamental trade-off between minimizing annotation cost and achieving fine-grained localization accuracy. Existing fully-supervised IML methods depend heavily on dense pixel-level mask annotations, which limits scalability to large datasets or real-wor
In offline-to-online (O2O) reinforcement learning, achieving efficient performance improvement while maintaining training stability remains a critical challenge for effective fine-tuning. Existing O2O methods usually focus on the balance between policy improvement and policy constraint during online