Zobrazeno 1 - 10
of 6 363
pro vyhledávání: '"Stricker, P. A."'
The primary challenge in Video Object Detection (VOD) is effectively exploiting temporal information to enhance object representations. Traditional strategies, such as aggregating region proposals, often suffer from feature variance due to the inclus
Externí odkaz:
http://arxiv.org/abs/2412.04915
Autor:
Wang, Shaoxiang, Xie, Yaxu, Chang, Chun-Peng, Millerdurai, Christen, Pagani, Alain, Stricker, Didier
Neural implicit fields have recently emerged as a powerful representation method for multi-view surface reconstruction due to their simplicity and state-of-the-art performance. However, reconstructing thin structures of indoor scenes while ensuring r
Externí odkaz:
http://arxiv.org/abs/2412.00242
Autor:
Sinha, Sankalp, Khan, Mohammad Sadil, Usama, Muhammad, Sam, Shino, Stricker, Didier, Ali, Sk Aziz, Afzal, Muhammad Zeshan
Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive datase
Externí odkaz:
http://arxiv.org/abs/2411.17945
In autonomous driving, environment perception has significantly advanced with the utilization of deep learning techniques for diverse sensors such as cameras, depth sensors, or infrared sensors. The diversity in the sensor stack increases the safety
Externí odkaz:
http://arxiv.org/abs/2411.17610
The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual privacy. Event cameras were initially considered as a promising solution
Externí odkaz:
http://arxiv.org/abs/2411.16440
Mastering the challenge of predicting properties of unknown materials with multiple principal elements (high entropy alloys/compositionally complex solid solutions) is crucial for the speedup in materials discovery. We show and discuss three models,
Externí odkaz:
http://arxiv.org/abs/2411.05466
Autor:
Aboukhadra, Ahmed Tawfik, Robertini, Nadia, Malik, Jameel, Elhayek, Ahmed, Reis, Gerd, Stricker, Didier
Surgery monitoring in Mixed Reality (MR) environments has recently received substantial focus due to its importance in image-based decisions, skill assessment, and robot-assisted surgery. Tracking hands and articulated surgical instruments is crucial
Externí odkaz:
http://arxiv.org/abs/2410.01293
Autor:
Sarode, Shalini, Khan, Muhammad Saif Ullah, Shehzadi, Tahira, Stricker, Didier, Afzal, Muhammad Zeshan
We propose ClassroomKD, a novel multi-mentor knowledge distillation framework inspired by classroom environments to enhance knowledge transfer between student and multiple mentors. Unlike traditional methods that rely on fixed mentor-student relation
Externí odkaz:
http://arxiv.org/abs/2409.20237
Autor:
Khan, Muhammad Saif Ullah, Khan, Muhammad Ahmed Ullah, Afzal, Muhammad Zeshan, Stricker, Didier
This paper reformulates cross-dataset human pose estimation as a continual learning task, aiming to integrate new keypoints and pose variations into existing models without losing accuracy on previously learned datasets. We benchmark this formulation
Externí odkaz:
http://arxiv.org/abs/2409.20469
Autor:
Khan, Mohammad Sadil, Sinha, Sankalp, Sheikh, Talha Uddin, Stricker, Didier, Ali, Sk Aziz, Afzal, Muhammad Zeshan
Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework fo
Externí odkaz:
http://arxiv.org/abs/2409.17106