---
title: "DM-POSA: Enhancing Open-World Test-Time Adaptation with Dual-Mode Matching and Prompt-Based Open Set Adaptation"
authors:
  - "Shiji Zhao"
  - "Shao-Yuan Li"
  - "Chuanxing Geng"
  - "Sheng-Jun Huang"
  - "Songcan Chen"
doi: "10.24963/ijcai.2025/790"
url: "https://ijcai.org/proceedings/2025/790"
pdf: "https://ijcai.org/proceedings/2025/0790.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "7101-7109"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Multi-task and transfer learning"
  - "Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"
  - "Machine Learning: ML: Open-World/Open-Set/OOD Learning"
---

# DM-POSA: Enhancing Open-World Test-Time Adaptation with Dual-Mode Matching and Prompt-Based Open Set Adaptation

Shiji Zhao, Shao-Yuan Li, Chuanxing Geng, Sheng-Jun Huang, Songcan Chen

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 7101-7109. DOI: [10.24963/ijcai.2025/790](https://doi.org/10.24963/ijcai.2025/790)

## Abstract

The need to generalize the pre-trained deep learning models to unknown test-time data distributions has spurred research into test-time adaptation (TTA). Existing studies have mainly focused
 on closed-set TTA with only covariate shifts, while largely overlooking open-set TTA that involves semantic shifts, i.e., unknown open-set classes. However, addressing adaptation to unknown classes is crucial for open-world safety-critical applications such as autonomous driving. In this paper, we emphasize that accurate identification of the open-set samples is rather challenging in TTA. The entanglement of semantic shift and covariate shift mutually confuse the network’s discriminative capability. This co-interference further exacerbates considering the single-pass data nature and low latency requirements. With this under standing, we propose Dual-mode Matching and Prompt-based Open Set Adaptation (DM-POSA) for open-set TTA to enhance discriminative feature learning and unknown classes distinguishment with minimal time cost. DM-POSA identifies open-set samples via dual-mode matching strategies, including model-parameter-based and feature space-based matching. It also optimizes the model with a random pairing discrepancy loss, enhancing the distributional difference between open-set and closed-set samples, thus improving the model’s ability to recognize unknown categories. Extensive
 experiments show the superiority of DM-POSA over state-of-the-art baselines on both closed-set class adaptation and open-set class detection.