DCRM-ViT is proposed, a domain-conditioned residual modulation framework for Vision Transformers that preserves general-vision knowledge while adapting to diverse medical and natural domains and achieves strong cross-domain performance with low overhead.
Blind Image Quality Assessment (BIQA) models trained on one distortion distribution often degrade when exposed to new ones, making sequential adaptation without forgetting a fundamental challenge. While continual learning offers a natural solution, existing methods typically retrain the entire backbone per task, limiti...
Satish Maurya, Parimala Kancharla· International Conference on...· 0 citations
A layer-wise uncertainty-guided feature perturbation and refinement framework that operates directly in representation space and can be seamlessly integrated into pretrained transformer encoders is proposed, validating the effectiveness of uncertainty-guided representation diffusion for medical visual understanding.
Xin Sun, Sheng-Nan Zou, Xin-Chun Wu et al.· Journal of Electronic Imagin...· 0 citations
Low-Rank Convolutional Adaptation (LoCA), a convolution-aware PEFT framework that addresses spatial-channel entanglement by decoupling channel and spatial adaptation and achieves competitive or state-of-the-art performance across fine-grained classification, domain-generalized semantic segmentation, and generative benc...
Sojung An, Jun-Ha Lee, Sujeong You et al.· arXiv.org· 0 citations
Efficient Unsupervised Domain Adaptation (EUDA) is proposed, a parameter-efficient framework that leverages a frozen DINOv2 backbone as a feature extractor and updates only a lightweight bottleneck and classification head to promote both discriminative learning and cross-domain alignment.
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang et al.· International Journal of Mac...· 9 citations
Vision-language models (VLMs) have significantly advanced open-vocabulary image understanding by learning aligned representations from large-scale image-text datasets. Despite their zero-shot generalization capabilities, adapting these foundation models to specific application domains remains challenging. Full fine-tun...
M. A. Aydın, Furkan Genç, Efe Özdilek et al.· Signal Processing and Commun...· 0 citations
TestMate is introduced, a novel, real-time, and backpropagation-free TTDA framework that enables immediate adaptation from the first frame, inherently avoids catastrophic forgetting and effectively preserves fine object details and boundaries, even for small objects.
Dimitrios Fotiou, V. Mygdalis, I. Pitas· 0 citations
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