27 citations · 47 across the 16 of their papers we have counts for
5 papers · 1 filter
Class Uncertainty: A Measure to Mitigate Class Imbalance
Z. S. Baltaci, K. Oksuz, S. Kuzucu +5
Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long…
Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation
Francisco Eiras, Kemal Oksuz, Adel Bibi +2
Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervi…
MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection
Kemal Oksuz, Selim Kuzucu, Tom Joy +1
Combining the strengths of many existing predictors to obtain a Mixture of Experts which is superior to its individual components is an effective way to improve the performance wit…
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration
Kemal Oksuz, Tom Joy, Puneet K. Dokania
The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using…
Correlation Loss: Enforcing Correlation between Classification and Localization
Fehmi Kahraman, Kemal Oksuz, Sinan Kalkan +1
Object detectors are conventionally trained by a weighted sum of classification and localization losses. Recent studies (e.g., predicting IoU with an auxiliary head, Generalized Fo…