Aug 2026· International Conference on Multimedia Analysis and Pattern Recognition· pp. 109-114· 0 citations· 22 references
Abstract
Unsupervised Domain Adaptation (UDA) for object detection remains challenging under adverse weather due to significant distribution shifts. While recent Vision Foundation Model (VFM) based methods show promise, they often encounter limitations in extreme domain gaps and pseudo-label noise. This paper proposes two enhancements to the DINO Teacher framework: (1) a multi-round self-training strategy to refine the labeling model progressively, and (2) a depth-guided spatial modulation mechanism using geometric priors from a DINOv3based depth estimator. By modulating the input space, the student model is encouraged to emphasize spatial cues that are less sensitive to visibility degradation in foggy environments. Experiments on Foggy Cityscapes demonstrate that our approach reaches 56.5% mAP with a VGG-16 backbone and 59.8% mAP with ResNet-50. These results demonstrate competitive performance compared with the DINO Teacher baseline and recent Vision Language Model (VLM) based methods, particularly for tail categories such as bus and train.
A test-time adaptation framework that requires no target-domain supervision that introduces two key innovations: a learnable dynamic augmentation module for better capturing target-domain characteristics, and a self-supervised adaptation strategy for leveraging unlabeled target-domain images by enforcing structural pri...
Yu-Huan Qi, Wen Wu, Yu-Chang Mo et al.· Multimedia Systems· 0 citations
Experimental results show that the proposed SFDA object detection framework for the YOLO family of single-stage detectors consistently outperforms the baseline on multiple cross-domain detection tasks, demonstrating its effectiveness and good generalization ability under the source-free setting.
This work hypothesizes that generative image editing can simulate a controlled domain shift in training data, effectively bridging the gap between source and target domains, and studies camouflaged military vehicle detection as a challenging domain shift scenario.
Isabel D. Stein, Thijs A. Eker, Sebastiaan P. Snel et al.· Artificial Intelligence for...· 0 citations
Multimodal fusion methods have shown great potential in remote sensing image analysis, but existing approaches rely heavily on massive amounts of annotated data. This is not only costly and time-consuming but also prone to subjective bias. To address this issue, we propose a category-prior-based self-supervised framewo...
Jia-Hang Liu, Jian Cui, Mao-yin Guo et al.· IEEE Transactions on Geoscie...· 0 citations
This work introduces SAM-AD, a domain-specific pretraining strategy that fine-tunes SAM on autonomous-driving imagery to extract feature representations with rich semantic information, and develops the Depth-Guided Wavelet Attention (DGWA) module, which suppresses high-frequency sensor noise while preserving critical c...
Zi-Ying Song, Lin Liu, Hong-Yu Pan et al.· 0 citations
