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arXiv · 2603.05964

QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection

Jinyeong Park·Donghwa Kang·Seunghwan An·Insoo Kim·Brent ByungHoon Kang·Hyeongboo Baek·Jibum Kim
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Abstract

Quantizing open-vocabulary object detection (OVOD) models reduces their memory and computational costs, but extremely low-bit quantization severely degrades both cross-modal (region-text) and intra-modal (region-region) alignments. This multimodal degradation is a unique challenge that prior quantization methods for closed-vocabulary detectors fail to resolve. To overcome this, we propose Quantization-Aware Training with Multimodal Alignment (QATMA), the first multimodal-aware and architecture-agnostic QAT framework tailored for OVOD. QATMA integrates two key components: (i) Curriculum QAT, which partitions the detector by functional roles and progressively expands the quantization scope to suppress error accumulation and ensure stable optimization; and (ii) Text-anchored Pairwise Similarity Distillation, which transfers both region-text and region-region alignments from a full-precision teacher model via pairwise cosine similarities in the joint embedding space. Experimental results on LVIS and COCO zero-shot benchmarks demonstrate that QATMA significantly outperforms existing QAT baselines under extremely low-bit settings, achieving gains of up to 4.3 and 7.6 AP, respectively.

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Jinyeong Park, Donghwa Kang, Seunghwan An, Insoo Kim, Brent ByungHoon Kang, Hyeongboo Baek, Jibum Kim. 2026-03-06. QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection. https://arxiv.org/abs/2603.05964

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