11 citations · 24 across the 12 of their papers we have counts for
12 papers
BenthicFlow: Generating Extensible Underwater Environments via Flow Matching
Joaquín Figueira, Camile Lendering, Manfred Gonzalez-Hernandez +3
Computer vision applications for 3D scene understanding in underwater environments remain challenging due to the lack of high-quality 3D data and the inability of surface-trained m…
ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection
Camile Lendering, Erkut Akdag, Joaquín Figueira +1
Unified anomaly detection requires modeling highly heterogeneous normal data without access to anomalous samples. While foundation models like DINOv2 provide rich token representat…
Boxes2Pixels: Learning Defect Segmentation from Noisy SAM Masks
Camile Lendering, Erkut Akdag, Egor Bondarev
Accurate defect segmentation is critical for industrial inspection, yet dense pixel-level annotations are rarely available. A common workaround is to convert inexpensive bounding b…
F3G-Avatar : Face Focused Full-body Gaussian Avatar
Willem Menu, Erkut Akdag, Pedro Quesado +2
Existing full-body Gaussian avatar methods primarily optimize global reconstruction quality and often fail to preserve fine-grained facial geometry and expression details. This cha…
R3PM-Net: Real-time, Robust, Real-world Point Matching Network
Yasaman Kashefbahrami, Erkut Akdag, Panagiotis Meletis +3
Accurate Point Cloud Registration (PCR) is an important task in 3D data processing, involving the estimation of a rigid transformation between two point clouds. While deep-learning…
LiveStre4m: Feed-Forward Live Streaming of Novel Views from Unposed Multi-View Video
Pedro Quesado, Erkut Akdag, Yasaman Kashefbahrami +2
Live-streaming Novel View Synthesis (NVS) from unposed multi-view video remains an open challenge in a wide range of applications. Existing methods for dynamic scene representation…
SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling
Camile Lendering, Erkut Akdag, Egor Bondarev
Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achieve strong results employing fou…
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance
Erkut Akdag, Egor Bondarev, Peter H. N. De With
Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications…
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos
Yiling Zhang, Erkut Akdag, Egor Bondarev +1
Detection of anomaly events is relevant for public safety and requires a combination of fine-grained motion information and contextual events at variable time-scales. To this end,…
Density-Guided Label Smoothing for Temporal Localization of Driving Actions
Tunc Alkanat, Erkut Akdag, Egor Bondarev +1
Temporal localization of driving actions plays a crucial role in advanced driver-assistance systems and naturalistic driving studies. However, this is a challenging task due to str…
Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition
Erkut Akdag, Zeqi Zhu, Egor Bondarev +1
Classification and localization of driving actions over time is important for advanced driver-assistance systems and naturalistic driving studies. Temporal localization is challeng…
Detection of Object Throwing Behavior in Surveillance Videos
Ivo P. C. Kersten, Erkut Akdag, Egor Bondarev +1
Anomalous behavior detection is a challenging research area within computer vision. Progress in this area enables automated detection of dangerous behavior using surveillance camer…