Zobrazeno 1 - 10
of 8 407
pro vyhledávání: '"Zablocki"'
Vision Language Models (VLMs) have demonstrated remarkable capabilities in various open-vocabulary tasks, yet their zero-shot performance lags behind task-specific finetuned models, particularly in complex tasks like Referring Expression Comprehensio
Externí odkaz:
http://arxiv.org/abs/2409.11919
In autonomous driving, motion prediction aims at forecasting the future trajectories of nearby agents, helping the ego vehicle to anticipate behaviors and drive safely. A key challenge is generating a diverse set of future predictions, commonly addre
Externí odkaz:
http://arxiv.org/abs/2409.11172
Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data, hindering their large-scale deployment. While increasing real-world training data coverage could address this iss
Externí odkaz:
http://arxiv.org/abs/2409.07830
Autor:
Xu, Yihong, Zablocki, Éloi, Boulch, Alexandre, Puy, Gilles, Chen, Mickael, Bartoccioni, Florent, Samet, Nermin, Siméoni, Oriane, Gidaris, Spyros, Vu, Tuan-Hung, Bursuc, Andrei, Valle, Eduardo, Marlet, Renaud, Cord, Matthieu
Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-to-end forecasting, the model must jointly detect and track from sensor
Externí odkaz:
http://arxiv.org/abs/2406.08113
Autor:
Feng, Lan, Bahari, Mohammadhossein, Amor, Kaouther Messaoud Ben, Zablocki, Éloi, Cord, Matthieu, Alahi, Alexandre
Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored. While these questions can be st
Externí odkaz:
http://arxiv.org/abs/2403.15098
Autor:
Chen, Ruohui, Rosenberg, Dori, Di, Chongzhi, Zablocki, Rong, Hartman, Sheri J, Lacroix, Andrea, Tu, Xin, Natarajan, Loki, Liu, Lin
In recent years, wearable devices have become more common to capture a wide range of health behaviors, especially for physical activity and sedentary behavior. These sensor-based measures are deemed to be objective and thus less prone to self-reporte
Externí odkaz:
http://arxiv.org/abs/2403.01000
Autor:
Chambon, Loick, Zablocki, Eloi, Chen, Mickael, Bartoccioni, Florent, Perez, Patrick, Cord, Matthieu
Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various downstream tasks. However, conventional models use grids with fixed resolu
Externí odkaz:
http://arxiv.org/abs/2312.00703