CR-QAT: Curriculum Relational Quantization-Aware Training for Open-Vocabulary Object Detection

• URL: http://arxiv.org/abs/2603.05964v2
• Date: Mon, 09 Mar 2026 07:42:44 GMT
• Title: CR-QAT: Curriculum Relational Quantization-Aware Training for Open-Vocabulary Object Detection
• Abstract summary: We propose curriculum relational quantization-aware training (CR-QAT)<n>CR-QAT mitigates error accumulation by partitioning the model for progressive quantization, ensuring stable optimization via error isolation.<n>Experiments on LVIS and zero-shot benchmarks demonstrate that CR-QAT consistently outperforms existing QAT baselines under aggressive low-bit settings.
• Score: 2.751147407342378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Open-vocabulary object detection (OVOD) enables novel category detection via vision-language alignment, but massive model sizes hinder deployment on resource-constrained devices. While quantization offers practical compression, we reveal that naive extreme low-bit (e.g., 4-bit) quantization severely degrades fine-grained vision-language alignment and distorts inter-region relational structures. To address this, we propose curriculum relational quantization-aware training (CR-QAT), an integrated framework combining stage-by-stage optimization with relational knowledge distillation. Within CR-QAT, curriculum QAT (CQAT) mitigates error accumulation by partitioning the model for progressive quantization, ensuring stable optimization via error isolation. Concurrently, text-centric relational KD (TRKD) is applied to task-relevant modules. By constructing text-anchored pairwise similarity matrices, TRKD comprehensively transfers the teacher's multi-dimensional relational knowledge. Experiments on LVIS and COCO zero-shot benchmarks demonstrate that CR-QAT consistently outperforms existing QAT baselines under aggressive low-bit settings, achieving relative AP improvements of up to 38.9% and 40.9%, respectively.

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