GraFT: Gradual Fusion Transformer for Multimodal Re-Identification

Haoli Yin, Emily Li, Eva Schiller, Luke McDermott, Daniel Cummings

Abstract: Object Re-Identification (ReID) is pivotal in computer vision, witnessing an escalating demand for adept multimodal representation learning. Current models, although promising, reveal scalability limitations with increasing modalities as they rely heavily on late fusion, which postpones the integration of specific modality insights. Addressing this, we introduce the \textbf{Gradual Fusion Transformer (GraFT)} for multimodal ReID. At its core, GraFT employs learnable fusion tokens that guide self-attention across encoders, adeptly capturing both modality-specific and object-specific features. Further bolstering its efficacy, we introduce a novel training paradigm combined with an augmented triplet loss, optimizing the ReID feature embedding space. We demonstrate these enhancements through extensive ablation studies and show that GraFT consistently surpasses established multimodal ReID benchmarks. Additionally, aiming for deployment versatility, we've integrated neural network pruning into GraFT, offering a balance between model size and performance. Most recent state-of-the-art multimodal ReID methods are not reproducible nor readily validated. To address this gap, we release our codebase to showcase a new state-of-the-art in reproducible multimodal ReID: \url{https://anonymous.4open.science/r/GraFT/}