76 citations · 109 across the 24 of their papers we have counts for
27 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…
Auditing Frame-Level AUC in Weakly Supervised Video Anomaly Detection: Granularity, Resolution, and Scene Bias
Sara Abdulaziz, Egor Bondarev
Frame-level area under the ROC curve (AUC) is the dominant evaluation metric for weakly supervised video anomaly detection (WSVAD). Its standard form measures whether an anomalous…
Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection
Sara Abdulaziz, Abdulrahman Al-Abri, Giacomo D'Amicantonio +1
Despite growing interest in weakly supervised video anomaly detection (WSVAD), current methods struggle to bridge the gap between coarse temporal supervision and fine-grained spati…
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…
AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection
Joaquín Figueira, Rob Van Gastel, Giacomo D'Amicantonio +4
Segmentation models in automated optical inspection of wire-bonded semiconductors are typically device-specific and must be re-trained when new devices or distribution shifts appea…
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…
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…
Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers
Cris Claessens, Christiaan Viviers, Giacomo D'Amicantonio +2
We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extrac…
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…
Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection
Sara Abdulaziz, Egor Bondarev
Advancements in deep learning have improved anomaly detection in surveillance videos, yet they raise urgent privacy concerns due to the collection of sensitive human data. In this…
Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
Sara Abdulaziz, Giacomo D'Amicantonio, Egor Bondarev
Recent advances in AI-powered surveillance have intensified concerns over the collection and processing of sensitive personal data. In response, research has increasingly focused o…
Just Dance with ! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-worl…
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…
Automated Camera Calibration via Homography Estimation with GNNs
Giacomo D'Amicantonio, Egor Bondarev, Peter H. N. De With
Over the past few decades, a significant rise of camera-based applications for traffic monitoring has occurred. Governments and local administrations are increasingly relying on th…
Dual Embedding Expansion for Vehicle Re-identification
Clint Sebastian, Raffaele Imbriaco, Egor Bondarev +1
Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point…
Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery
Clint Sebastian, Raffaele Imbriaco, Egor Bondarev +1
Building extraction from aerial images has several applications in problems such as urban planning, change detection, and disaster management. With the increasing availability of d…
Adversarial Loss for Semantic Segmentation of Aerial Imagery
Clint Sebastian, Raffaele Imbriaco, Egor Bondarev +1
Automatic building extraction from aerial imagery has several applications in urban planning, disaster management, and change detection. In recent years, several works have adopted…
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Raffaele Imbriaco, Clint Sebastian, Egor Bondarev +1
Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearl…