36 citations · 112 across the 14 of their papers we have counts for
14 papers
TopoMaskV3: 3D Mask Head with Dense Offset and Height Predictions for Road Topology Understanding
Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Mask-based paradigms for road topology understanding, such as TopoMaskV2, offer a complementary alternative to query-based methods by generating centerlines via a dense rasterized…
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu +4
Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable. Autonomous…
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection
Ozsel Kilinc, Cem Tarhan
Accurate, fast, and reliable 3D perception is essential for autonomous driving. Recently, bird's-eye view (BEV)-based perception approaches have emerged as superior alternatives to…
GLane3D : Detecting Lanes with Graph of 3D Keypoints
Halil İbrahim Öztürk, Muhammet Esat Kalfaoğlu, Ozsel Kilinc
Accurate and efficient lane detection in 3D space is essential for autonomous driving systems, where robust generalization is the foremost requirement for 3D lane detection algorit…
TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding
Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Understanding road topology is crucial for autonomous driving. This paper introduces TopoBDA (Topology with Bezier Deformable Attention), a novel approach that enhances road topolo…
TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem
M. Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Recently, the centerline has become a popular representation of lanes due to its advantages in solving the road topology problem. To enhance centerline prediction, we have develope…
TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture
M. Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Driving scene understanding task involves detecting static elements such as lanes, traffic signs, and traffic lights, and their relationships with each other. To facilitate the dev…
Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goals
Ozsel Kilinc, Giovanni Montana
Learning robot manipulation through deep reinforcement learning in environments with sparse rewards is a challenging task. In this paper we address this problem by introducing a no…
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations
Ozsel Kilinc, Giovanni Montana
Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions. Recent developments in this area have demonstrat…
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
Ozsel Kilinc, Giovanni Montana
Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-worl…
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
Ozsel Kilinc, Ismail Uysal
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specif…
GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning
Ozsel Kilinc, Ismail Uysal
In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised port…
Clustering-based Source-aware Assessment of True Robustness for Learning Models
Ozsel Kilinc, Ismail Uysal
We introduce a novel validation framework to measure the true robustness of learning models for real-world applications by creating source-inclusive and source-exclusive partitions…
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks
Ozsel Kilinc, Ismail Uysal
In this paper, we discuss a different type of semi-supervised setting: a coarse level of labeling is available for all observations but the model has to learn a fine level of laten…