3 citations · 5 across the 11 of their papers we have counts for
12 papers
MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors
Arda Alniak, Sinan Kalkan, Mustafa Mert Ankarali +2
Traditional monocular Visual-Inertial Odometry (VIO) systems struggle in low-texture environments where sparse visual features are insufficient for accurate pose estimation. To add…
FairSSL: Fair Multimodal Self-Supervised Learning
Jiaee Cheong, Abtin Mogharabin, Paul Liang +2
Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-relevant information. We argue that this…
L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation
Hazal Mogultay Ozcan, Sinan Kalkan, Fatos T. Yarman-Vural
In this paper, we propose a novel model called Learnable VAE (L-VAE), which learns a disentangled representation together with the hyperparameters of the cost function. L-VAE can b…
ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
Yusuf Meric Karadag, Ismail Talaz, Ipek Gursel Dino +1
The problem of Time-series Forecasting is generally addressed by recurrent, Transformer-based and the recently proposed Mamba-based architectures. However, existing architectures g…
PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval
Osman Tursun, Sinan Kalkan, Simon Denman +1
Zero-shot Composed Image Retrieval (ZS-CIR) enables image search using a reference image and a text prompt without requiring specialized text-image composition networks trained on…
Machine Learning Fairness for Depression Detection using EEG Data
Angus Man Ho Kwok, Jiaee Cheong, Sinan Kalkan +1
This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using differ…
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
Jiaee Cheong, Aditya Bangar, Sinan Kalkan +1
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitas…
BaSeNet: A Learning-based Mobile Manipulator Base Pose Sequence Planning for Pickup Tasks
Lakshadeep Naik, Sinan Kalkan, Sune L. Sørensen +2
In many applications, a mobile manipulator robot is required to grasp a set of objects distributed in space. This may not be feasible from a single base pose and the robot must pla…
Part-based Quantitative Analysis for Heatmaps
Osman Tursun, Sinan Kalkan, Simon Denman +2
Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhanc…
XoFTR: Cross-modal Feature Matching Transformer
Önder Tuzcuoğlu, Aybora Köksal, Buğra Sofu +2
We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images, TIR images are less suscep…
Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation
Barış Can Çam, Kemal Öksüz, Fehmi Kahraman +3
This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU) as a new measure for positive-negative assignment of anchor boxes during training of instance segmenta…
Uncertainty-based Fairness Measures
Selim Kuzucu, Jiaee Cheong, Hatice Gunes +1
Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means f…