Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection

2026-05-13 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, medium

Summary

Sangin Lee and colleagues introduce MS-DePro, a Multi-Source Detector with Depth and Prompt, designed to improve object detection in target domains that differ significantly from training data distributions. Traditional multi-source domain adaptation (MSDA) methods often struggle by learning domain-agnostic features from domain-specific RGB images. MS-DePro addresses this by leveraging domain-agnostic input modalities, specifically depth maps and text, to encode universal characteristics. The system employs depth maps for generating domain-agnostic region proposals for localization and integrates multi-modal features to align learnable text embeddings for classification. This approach achieves state-of-the-art performance on MSDA benchmarks, with comprehensive ablations confirming the effectiveness of its depth-guided localization and multi-modal guided prompt learning components. The code for MS-DePro is publicly available on GitHub.

Key takeaway

For research scientists developing robust object detection systems, consider integrating multi-modal inputs like depth and text to overcome domain shift challenges. MS-DePro demonstrates that using depth maps for localization and text embeddings for classification can significantly improve performance in multi-source domain adaptation scenarios, offering a path to more generalizable detectors. You should explore how these domain-agnostic modalities can enhance your model's ability to perform in diverse, unseen environments.

Key insights

Multi-modal inputs like depth and text can enhance multi-source domain adaptation for robust object detection.

Principles

• Separate processing of multiple source domains improves adaptation.
• Domain-agnostic inputs can guide feature learning.
• Aligning text embeddings aids classification across domains.

Method

MS-DePro uses depth maps for domain-agnostic region proposals and multi-modal guided prompt learning to align text embeddings for classification, addressing domain shift in object detection.

In practice

• Utilize depth maps for robust object localization.
• Integrate text embeddings for improved classification.
• Apply multi-modal inputs for domain adaptation.

Topics

• Multi-Source Domain Adaptation
• Object Detection
• Multi-Modal Learning
• Depth Maps
• Prompt Learning

Code references

• sejong-rcv/Multi-Modal-Guided-Multi-Source-Domain-Adaptation-for-Object-Detection
• ika-rwth-aachen/MultiCorrupt
• linaagh98/MSRNet
• lihongzhao99/SSMDG

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.