Multimodal Re-Identification (ReID) is a popular retrieval task that aims to
re-identify objects across diverse data streams, prompting many researchers to
integrate multiple modalities into a unified representation. While such fusion
promises a holistic view, our investigations shed light on potential pitfalls.
We uncover that prevailing late-fusion techniques often produce suboptimal
latent representations when compared to methods that train modalities in
isolation. We argue that this effect is largely due to the inadvertent
relaxation of the training objectives on individual modalities when using
fusion, what others have termed modality laziness. We present a nuanced
point-of-view that this relaxation can lead to certain modalities failing to
fully harness available task-relevant information, and yet, offers a protective
veil to noisy modalities, preventing them from overfitting to task-irrelevant
data. Our findings also show that unimodal concatenation (UniCat) and other
late-fusion ensembling of unimodal backbones, when paired with best-known
training techniques, exceed the current state-of-the-art performance across
several multimodal ReID benchmarks. By unveiling the double-edged sword of
"modality laziness", we motivate future research in balancing local modality
strengths with global representations.

多模态重新识别是一个流行的检索任务，旨在跨不同数据流重新识别对象，本研究发现现有的后期融合技术在与训练单独的模态比较时产生次优潜在表示，我们探讨了这种效应主要是由于融合使用时对个别模态的训练目标不经意地放松，他人称之为模态懒散。我们提出了一个细致的观点，即这种放松可能导致某些模态无法充分利用可用的任务相关信息，然而，对于噪声模态提供了保护屏障，防止它们过拟合到无关数据，我们的发现还表明，当与已知最佳训练技术配对时，单模态拼接（UniCat）和其他后期融合的单模态骨干集成在多个多模态 ReID 基准测试中超过了当前的最先进性能，通过揭示 “模态懒散” 的双刃剑，我们推动未来研究在平衡局部模态优势和全局表示方面。