6 papers
Learning What Helps: Task-Aligned Context Selection for Vision Tasks
Jingyu Guo, Emir Konuk, Fredrik Strand +2
Humans often resolve visual uncertainty by comparing an image with relevant examples, but ViTs lack the ability to identify which examples would improve their predictions. We prese…
Efficient Self-Supervised Adaptation for Medical Image Analysis
Moein Sorkhei, Emir Konuk, Jingyu Guo +3
Self-supervised adaptation (SSA) improves foundation model transfer to medical domains but is computationally prohibitive. Although parameter efficient fine-tuning methods such as…
k-NN as a Simple and Effective Estimator of Transferability
Moein Sorkhei, Christos Matsoukas, Johan Fredin Haslum +2
How well can one expect transfer learning to work in a new setting where the domain is shifted, the task is different, and the architecture changes? Many transfer learning metrics…
APLA: A Simple Adaptation Method for Vision Transformers
Moein Sorkhei, Emir Konuk, Kevin Smith +1
Existing adaptation techniques typically require architectural modifications or added parameters, leading to high computational costs and complexity. We introduce Attention Project…
Random Token Fusion for Multi-View Medical Diagnosis
Jingyu Guo, Christos Matsoukas, Fredrik Strand +1
In multi-view medical diagnosis, deep learning-based models often fuse information from different imaging perspectives to improve diagnostic performance. However, existing approach…
Learning from Offline Foundation Features with Tensor Augmentations
Emir Konuk, Christos Matsoukas, Moein Sorkhei +2
We introduce Learning from Offline Foundation Features with Tensor Augmentations (LOFF-TA), an efficient training scheme designed to harness the capabilities of foundation models i…