Evidential Curriculum Learning for Multi-Prompt Weakly Supervised RGB-T Salient Object Detection

Multi-prompt weakly supervised RGB-Thermal salient object detection (WSSOD) reduces annotation costs with points, boxes, and scribbles, but suffers from unreliable predictions and unstable training caused by incomplete prompts, cross-modal inconsistency, and imbalanced sample difficulty. To address these issues, we propose an Evidential Curriculum Learning Network (ECL-Net), which builds a unified uncertainty-driven learning pipeline from region-level reliability modeling to sample-level curriculum optimization. Specifically, Evidential Certain-Uncertain Refinement (ECUR) mechanism models Dirichlet evidence to decouple confident and uncertain regions and refines ambiguous features with certainty-weighted prototypes and intra-class self-attention. Evidential Uncertainty Curriculum Module (EUCM) reuses this uncertainty estimate for easy-to-hard sample scheduling, enabling stable RGB-T saliency priors before harder cases. Extensive experiments under point-, box-, and scribble-supervised settings on VT821, VT1000, and VT5000 demonstrate that ECL-Net achieves state-of-the-art performance against existing RGB-T WSSOD methods.