Multi-modal multi-source domain for detection

Sangin Lee

doi:10.1016/j.knosys.2026.116426

Multi-modal multi-source domain for detection

2026-06-01

0

PRE

AI

DOI：10.1016/j.knosys.2026.116426

Original

Original request for help

Save

Abstract

En 中文

General object detection (OD) struggles to detect objects in the target domain that differ from the training distribution. To address this, recent studies demonstrate that training from multiple source domains and explicitly processing them separately for multi-source domain adaptation (MSDA) outperforms blending them for unsupervised domain adaptation (UDA). However, existing MSDA methods learn domain-agnostic features from domain-specific RGB images while preserving domain-specific information from the domain-agnostic feature map. To address this, we propose MS-DePro: Multi-Source Detector with Depth and Prompt, composed of (1) depth-guided localization and (2) multi-modal guided prompt learning. We leverage domain-agnostic input modalities, namely depth maps and text, to encode domain-agnostic characteristics. Specifically, we utilize depth maps to generate domain-agnostic region proposals for localization and integrate multi-modal features to align learnable text embeddings for classification. MS-DePro achieves state-of-the-art performance on MSDA benchmarks, and comprehensive ablations demonstrate the effectiveness of our contributions. Our code is available on GitHub.

Keywords:

Object detection

Domain adaptation

Multi-source domain adaptation

Multi-modal multi-source domain for detection

Abstract

Journal

Organization

Cited Papers