PEFT-BoA: Parameter-Efficient Fine-Tuning with Bag-of-Adapters for Multi-Modal Object Re-identification

Hongchao Li; Guangxing Liu; Xixi Wang; Baihe Liang; YongLong Luo

AAAI 2026technical0 citations

PEFT-BoA: Parameter-Efficient Fine-Tuning with Bag-of-Adapters for Multi-Modal Object Re-identification

Hongchao Li, Guangxing Liu, Xixi Wang, Baihe Liang, YongLong Luo

Abstract

Multi-modal object Re-identification (ReID) aims to retrieve individuals by leveraging complementary information from different modalities. Recent CLIP-based approaches show promising results, but they usually employ prompt-based or hybrid prompt-adapter tuning and still face the problems of heterogeneous domain gap, fine-grained identity discrimination and noise instance interference. To address these problems, we introduce a novel Parameter-Efficient Fine-Tuning framework with Bag-of-Adapters (PEFT-BoA) based on the pre-trained CLIP

BibTeX

@inproceedings{aaai2026_peftboaparameter,
  title = {PEFT-BoA: Parameter-Efficient Fine-Tuning with Bag-of-Adapters for Multi-Modal Object Re-identification},
  author = {Hongchao Li and Guangxing Liu and Xixi Wang and Baihe Liang and YongLong Luo},
  booktitle = {AAAI 2026},
  year = {2026}
}