316 citations · 318 across the 12 of their papers we have counts for
13 papers
MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space
Markus Karmann, Shile Li, Christian Internò +10
Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction. However, standard JEPA train…
Joint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data Generation
Shile Li, Markus Karmann, Onay Urfalioglu
We present a framework for end-to-end joint quantization of Vision Transformers trained on ImageNet for the purpose of image classification. Unlike prior post-training or block-wis…
M2N2V2: Multi-Modal Unsupervised and Training-free Interactive Segmentation
Markus Karmann, Peng-Tao Jiang, Bo Li +1
We present Markov Map Nearest Neighbor V2 (M2N2V2), a novel and simple, yet effective approach which leverages depth guidance and attention maps for unsupervised and training-free…
Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation
Markus Karmann, Onay Urfalioglu
Recent progress in interactive point prompt based Image Segmentation allows to significantly reduce the manual effort to obtain high quality semantic labels. State-of-the-art unsup…
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description Extraction
Henrique Siqueira, Patrick Ruhkamp, Ibrahim Halfaoui +2
Learnable keypoint detectors and descriptors are beginning to outperform classical hand-crafted feature extraction methods. Recent studies on self-supervised learning of visual rep…
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
Fengyi Shen, Akhil Gurram, Ahmet Faruk Tuna +2
Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation. From domai…
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen +2
Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearanc…
Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention
Ibrahim Halfaoui, Fahd Bouzaraa, Onay Urfalioglu +1
Acquiring information about the road lane structure is a crucial step for autonomous navigation. To this end, several approaches tackle this task from different perspectives such a…
Multimodal End-to-End Autonomous Driving
Yi Xiao, Felipe Codevilla, Akhil Gurram +2
A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressin…
Learnable Exposure Fusion for Dynamic Scenes
Fahd Bouzaraa, Ibrahim Halfaoui, Onay Urfalioglu
In this paper, we focus on Exposure Fusion (EF) [ExposFusi2] for dynamic scenes. The task is to fuse multiple images obtained by exposure bracketing to create an image which compri…
Monocular Depth Estimation by Learning from Heterogeneous Datasets
Akhil Gurram, Onay Urfalioglu, Ibrahim Halfaoui +2
Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of…
Symmetry Breaking in Neuroevolution: A Technical Report
Onay Urfalioglu, Orhan Arikan
Artificial Neural Networks (ANN) comprise important symmetry properties, which can influence the performance of Monte Carlo methods in Neuroevolution. The problem of the symmetries…
Artificial Neural Networks, Symmetries and Differential Evolution
Onay Urfalioglu, Orhan Arikan
Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN)…