提升真实场景衣物变化行人重识别在低质量图像下的准确率
Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID
- 用粗粒度属性预测增强模型对外部细粒度特征的感知能力
- 在LaST和DeepChange数据集上提升1.6%-2.9%的Top-1准确率
- 适合关注真实世界行人重识别与鲁棒性改进的研究者
本工作聚焦于真实场景下的衣物变化行人重识别（CC-ReID）。现有方法在高质量（HQ）图像上表现良好，但在低质量（LQ）图像上表现不佳，常见噪声包括像素化、模糊和运动模糊。这些噪声不仅影响外部生物特征（如姿态、体型），还破坏模型内部特征表示，导致低质量图像特征聚集，难以区分，引发错误匹配。为此，提出新框架RLQ，通过交替执行粗粒度属性预测（CAP）与任务无关蒸馏（TAD）来提升模型鲁棒性。CAP通过粗略预测引入外部细粒度属性信息，降低噪声输入影响；TAD则通过外部数据集，在无任务依赖的自监督下桥接高低质量特征差距，优化内部表示。RLQ在真实数据集LaST、DeepChange上较现有方法提升1.6%-2.9% Top-1，在PRCC上提升5.3%-6%，同时在LTCC上保持竞争力。代码即将公开。
原文摘要 · Abstract (English)
This work focuses on Clothes Changing Re-IDentification (CC-ReID) for the real world. Existing works perform well with high-quality (HQ) images, but struggle with low-quality (LQ) where we can have artifacts like pixelation, out-of-focus blur, and motion blur. These artifacts introduce noise to not only external biometric attributes (e.g. pose, body shape, etc.) but also corrupt the model's internal feature representation. Models usually cluster LQ image features together, making it difficult to distinguish between them, leading to incorrect matches. We propose a novel framework Robustness against Low-Quality (RLQ) to improve CC-ReID model on real-world data. RLQ relies on Coarse Attributes Prediction (CAP) and Task Agnostic Distillation (TAD) operating in alternate steps in a novel training mechanism. CAP enriches the model with external fine-grained attributes via coarse predictions, thereby reducing the effect of noisy inputs. On the other hand, TAD enhances the model's internal feature representation by bridging the gap between HQ and LQ features, via an external dataset through task-agnostic self-supervision and distillation. RLQ outperforms the existing approaches by 1.6%-2.9% Top-1 on real-world datasets like LaST, and DeepChange, while showing consistent improvement of 5.3%-6% Top-1 on PRCC with competitive performance on LTCC. *The code will be made public soon.*
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