Title: C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: RGB-T Modality Complementarity, Cross-modal Alignment & Fusion, Robustness Enhancement, Open-Vocabulary Object Detection

Abstract: Object detection has advanced significantly in the closed-set setting, but real-world deployment remains limited by two challenges: poor generalization to unseen categories and insufficient robustness under adverse conditions. Prior research has explored these issues separately: visible--infrared detection  improves robustness but lacks generalization, while open-world detection leverages vision--language alignment strategy for category diversity but struggles under extreme environments. This trade-off leaves robustness and diversity difficult to achieve simultaneously. To mitigate these issues, we propose C3-OWD, a curriculum cross-modal contrastive learning framework that unifies both strengths. Stage 1 enhances robustness by pretraining with RGBT data, while Stage 2 improves generalization via vision--language alignment. To prevent catastrophic forgetting between two stages, we introduce an Exponential Moving Average (EMA) mechanism that theoretically guarantees preservation of pre-stage performance with bounded parameter lag and function consistency. Experiments on FLIR, OV-COCO, and OV-LVIS demonstrate the effectiveness of our approach: C3-OWD achieves $80.1$ AP$^{50}$ on FLIR, $48.6$ AP$^{50}_{\text{Novel}}$ on OV-COCO, and $35.7$ mAP$_r$ on OV-LVIS, establishing competitive performance across both robustness and diversity benchmarks.