EW-DETR: Evolving World Object Detection via Incremental Low-Rank DEtection TRansformer

• URL: http://arxiv.org/abs/2602.20985v1
• Date: Tue, 24 Feb 2026 15:06:04 GMT
• Title: EW-DETR: Evolving World Object Detection via Incremental Low-Rank DEtection TRansformer
• Authors: Munish Monga, Vishal Chudasama, Pankaj Wasnik, C. V. Jawahar,
• Abstract summary: We introduce Evolving World Object Detection (EWOD), a paradigm coupling incremental learning, domain adaptation, and unknown detection under exemplar-free constraints.<n>We propose EW-DETR framework that augments DETR-based detectors with three synergistic modules.<n>This framework generalises across DETR-based detectors, enabling state-of-the-art RF-DETR to operate effectively in evolving-world settings.
• Score: 21.633220257780522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world object detection must operate in evolving environments where new classes emerge, domains shift, and unseen objects must be identified as "unknown": all without accessing prior data. We introduce Evolving World Object Detection (EWOD), a paradigm coupling incremental learning, domain adaptation, and unknown detection under exemplar-free constraints. To tackle EWOD, we propose EW-DETR framework that augments DETR-based detectors with three synergistic modules: Incremental LoRA Adapters for exemplar-free incremental learning under evolving domains; a Query-Norm Objectness Adapter that decouples objectness-aware features from DETR decoder queries; and Entropy-Aware Unknown Mixing for calibrated unknown detection. This framework generalises across DETR-based detectors, enabling state-of-the-art RF-DETR to operate effectively in evolving-world settings. We also introduce FOGS (Forgetting, Openness, Generalisation Score) to holistically evaluate performance across these dimensions. Extensive experiments on Pascal Series and Diverse Weather benchmarks show EW-DETR outperforms other methods, improving FOGS by 57.24%.

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