3 citations · 3 across the 7 of their papers we have counts for
8 papers
The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models
Robert Welch, Emir Konuk, Kevin Smith
Vision-language models (VLMs) are increasingly deployed in high-stakes settings where reliable uncertainty quantification (UQ) is as important as predictive accuracy. Extended reas…
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…
VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings
Leonardo Scabini, Kallil M. Zielinski, Emir Konuk +4
Texture recognition has recently been dominated by ImageNet-pre-trained deep Convolutional Neural Networks (CNNs), with specialized modifications and feature engineering required t…
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…
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…
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Christos Matsoukas, Albert Bou I Hernandez, Yue Liu +8
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning o…
An empirical study of the relation between network architecture and complexity
Emir Konuk, Kevin Smith
In this preregistration submission, we propose an empirical study of how networks handle changes in complexity of the data. We investigate the effect of network capacity on general…