Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Debattista, Kurt"'
Capturing real world lighting is a long standing challenge in imaging and most practical methods acquire High Dynamic Range (HDR) images by either fusing multiple exposures, or boosting the dynamic range of Standard Dynamic Range (SDR) images. Multip
Externí odkaz:
http://arxiv.org/abs/2406.05475
Autor:
Goswami, Abhishek, Singh, Aru Ranjan, Banterle, Francesco, Debattista, Kurt, Bashford-Rogers, Thomas
The range of real-world scene luminance is larger than the capture capability of many digital camera sensors which leads to details being lost in captured images, most typically in bright regions. Inverse tone mapping attempts to boost these captured
Externí odkaz:
http://arxiv.org/abs/2405.15468
While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in safety-critical applications, such as automated driving systems (ADS). C
Externí odkaz:
http://arxiv.org/abs/2404.18326
Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving simulators offer a solution by automatically generating various drivin
Externí odkaz:
http://arxiv.org/abs/2404.09111
Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during sampling. However, DMs often fail to realistically model Lidar raydro
Externí odkaz:
http://arxiv.org/abs/2404.05505
Autor:
Wang, Yiting, Zhao, Haonan, Gummadi, Daniel, Dianati, Mehrdad, Debattista, Kurt, Donzella, Valentina
Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique to identify and categorise objects, impending hazards, and driveable s
Externí odkaz:
http://arxiv.org/abs/2402.15469
Perception sensor models are essential elements of automotive simulation environments; they also serve as powerful tools for creating synthetic datasets to train deep learning-based perception models. Developing realistic perception sensor models pos
Externí odkaz:
http://arxiv.org/abs/2312.15817
Due to the costliness of labelled data in real-world applications, semi-supervised learning, underpinned by pseudo labelling, is an appealing solution. However, handling confusing samples is nontrivial: discarding valuable confusing samples would com
Externí odkaz:
http://arxiv.org/abs/2312.01169
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifications required to cross the model's decision boundary. Current deep ge
Externí odkaz:
http://arxiv.org/abs/2307.15786
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by the inherent lack of robust verification techniques to assure their perf
Externí odkaz:
http://arxiv.org/abs/2305.16532