4 citations · 5 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation
Johan Fredin Haslum, Christos Matsoukas, Karl-Johan Leuchowius +1
High Content Imaging (HCI) plays a vital role in modern drug discovery and development pipelines, facilitating various stages from hit identification to candidate drug characteriza…