Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Segù, Mattia"'
Autor:
Li, Siyuan, Ke, Lei, Yang, Yung-Hsu, Piccinelli, Luigi, Segù, Mattia, Danelljan, Martin, Van Gool, Luc
Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. Currently, the best-performing methods are mainly based on pure appearance matching. Due to the complexity of motion patterns in t
Externí odkaz:
http://arxiv.org/abs/2409.11235
Autor:
Li, Siyuan, Ke, Lei, Danelljan, Martin, Piccinelli, Luigi, Segu, Mattia, Van Gool, Luc, Yu, Fisher
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT). Current methods predominantly rely on labeled domain-specific video datasets, which limits t
Externí odkaz:
http://arxiv.org/abs/2406.04221
Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene representation limited to the image plane, recent approaches based on radi
Externí odkaz:
http://arxiv.org/abs/2404.03658
Autor:
Piccinelli, Luigi, Yang, Yung-Hsu, Sakaridis, Christos, Segu, Mattia, Li, Siyuan, Van Gool, Luc, Yu, Fisher
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to
Externí odkaz:
http://arxiv.org/abs/2403.18913
Continual learning allows a model to learn multiple tasks sequentially while retaining the old knowledge without the training data of the preceding tasks. This paper extends the scope of continual learning research to class-incremental learning for m
Externí odkaz:
http://arxiv.org/abs/2310.03006
Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving, and its robustness to unseen conditions is a requirement to avoid life-critical failures. Despite the urge of safety in driving systems, no soluti
Externí odkaz:
http://arxiv.org/abs/2310.01926
Autor:
Fan, Qi, Segu, Mattia, Tai, Yu-Wing, Yu, Fisher, Tang, Chi-Keung, Schiele, Bernt, Dai, Dengxin
Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary substantially due to environment changes and sensor noises, but deep models
Externí odkaz:
http://arxiv.org/abs/2211.04393
Autor:
Sun, Tao, Segu, Mattia, Postels, Janis, Wang, Yuxuan, Van Gool, Luc, Schiele, Bernt, Tombari, Federico, Yu, Fisher
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video driving datasets, however, fall short of capturing the mutable nature of the real world. In th
Externí odkaz:
http://arxiv.org/abs/2206.08367
Autor:
Zaheer, Muhammad Zaigham, Mahmood, Arif, Khan, Muhammad Haris, Segu, Mattia, Yu, Fisher, Lee, Seung-Ik
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less frequent in occurrence and usuall
Externí odkaz:
http://arxiv.org/abs/2203.03962
Autor:
Postels, Janis, Segu, Mattia, Sun, Tao, Sieber, Luca, Van Gool, Luc, Yu, Fisher, Tombari, Federico
A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Networks. On the premise of informative representations, these determinis
Externí odkaz:
http://arxiv.org/abs/2107.00649