Zobrazeno 1 - 10
of 1 570
pro vyhledávání: '"multi-modal dataset"'
Autor:
Gamerdinger, Jörg, Teufel, Sven, Schulz, Patrick, Amann, Stephan, Kirchner, Jan-Patrick, Bringmann, Oliver
Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous driving. In order to develop and test novel collective perception technologi
Externí odkaz:
http://arxiv.org/abs/2408.03065
Autor:
Karvat, Mateus, Givigi, Sidney
Adverse weather conditions pose a significant challenge to the widespread adoption of Autonomous Vehicles (AVs) by impacting sensors like LiDARs and cameras. Even though Collaborative Perception (CP) improves AV perception in difficult conditions, ex
Externí odkaz:
http://arxiv.org/abs/2410.06380
Autor:
Zhuang, Lipeng, Fan, Shiyu, Ru, Yingdong, Audonnet, Florent, Henderson, Paul, Aragon-Camarasa, Gerardo
We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations of flattening and folding 44 unique garments across 8 categories, Flat
Externí odkaz:
http://arxiv.org/abs/2409.18297
We introduce a new, highly challenging benchmark and a dataset -- FungiTastic -- based on data continuously collected over a twenty-year span. The dataset originates in fungal records labeled and curated by experts. It consists of about 350k multi-mo
Externí odkaz:
http://arxiv.org/abs/2408.13632
Autor:
Mohanty, Shrestha, Arabzadeh, Negar, Tupini, Andrea, Sun, Yuxuan, Skrynnik, Alexey, Zholus, Artem, Côté, Marc-Alexandre, Kiseleva, Julia
Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing interactive agents capable of understanding and executing grounded natural language instruct
Externí odkaz:
http://arxiv.org/abs/2407.08898
Autor:
Li, Bang, Luo, Donghao, Liang, Yujie, Yang, Jing, Ding, Zengmao, Peng, Xu, Jiang, Boyuan, Han, Shengwei, Sui, Dan, Qin, Peichao, Wu, Pian, Wang, Chaoyang, Qi, Yun, Jin, Taisong, Wang, Chengjie, Huang, Xiaoming, Shu, Zhan, Ji, Rongrong, Liu, Yongge, Wu, Yunsheng
Oracle bone inscriptions(OBI) is the earliest developed writing system in China, bearing invaluable written exemplifications of early Shang history and paleography. However, the task of deciphering OBI, in the current climate of the scholarship, can
Externí odkaz:
http://arxiv.org/abs/2407.03900
We present a novel synthetically generated multi-modal dataset, SCaRL, to enable the training and validation of autonomous driving solutions. Multi-modal datasets are essential to attain the robustness and high accuracy required by autonomous systems
Externí odkaz:
http://arxiv.org/abs/2405.17030
Autor:
Tölle, Malte, Burger, Lukas, Kelm, Halvar, André, Florian, Bannas, Peter, Diller, Gerhard, Frey, Norbert, Garthe, Philipp, Groß, Stefan, Hennemuth, Anja, Kaderali, Lars, Krüger, Nina, Leha, Andreas, Martin, Simon, Meyer, Alexander, Nagel, Eike, Orwat, Stefan, Scherer, Clemens, Seiffert, Moritz, Seliger, Jan Moritz, Simm, Stefan, Friede, Tim, Seidler, Tim, Engelhardt, Sandy
Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging m
Externí odkaz:
http://arxiv.org/abs/2407.09064
Autor:
Armanious, Karim, Quach, Maurice, Ulrich, Michael, Winterling, Timo, Friesen, Johannes, Braun, Sascha, Jenet, Daniel, Feldman, Yuri, Kosman, Eitan, Rapp, Philipp, Fischer, Volker, Sons, Marc, Kohns, Lukas, Eckstein, Daniel, Egbert, Daniela, Letsch, Simone, Voege, Corinna, Huttner, Felix, Bartler, Alexander, Maiwald, Robert, Lin, Yancong, Rüegg, Ulf, Gläser, Claudius, Bischoff, Bastian, Freess, Jascha, Haug, Karsten, Klee, Kathrin, Caesar, Holger
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a unique inte
Externí odkaz:
http://arxiv.org/abs/2407.12803
The development of multi-modal object detection for Unmanned Aerial Vehicles (UAVs) typically relies on a large amount of pixel-aligned multi-modal image data. However, existing datasets face challenges such as limited modalities, high construction c
Externí odkaz:
http://arxiv.org/abs/2406.06230