C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection

• URL: http://arxiv.org/abs/2509.23316v1
• Date: Sat, 27 Sep 2025 14:04:15 GMT
• Title: C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection
• Abstract summary: Object detection has advanced significantly, but real-world deployment remains limited by two challenges: poor generalization to unseen categories and insufficient robustness under adverse conditions.<n>We propose textbfC3-OWD, a curriculum cross-modal contrastive learning framework that unifies both strengths.<n>Experiments on FLIR, OV-COCO, and OV-LVIS demonstrate the effectiveness of our approach.
• Score: 34.63917792892542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \textbf{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 evaluations. Code available at: https://github.com/justin-herry/C3-OWD.git.

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