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Found 14 papers, 7 papers with code
Exploring Generative AI for Sim2Real in Driving Data Synthesis
A comparative analysis of these methods is presented from the perspective of image quality and perception.
Taming Transformers for Realistic Lidar Point Cloud Generation
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.
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving
Motivated by such a need, this work proposes a unifying pipeline to assess the robustness of panoptic segmentation models for AAD, correlating it with traditional image quality.
Contrastive Learning-Based Framework for Sim-to-Real Mapping of Lidar Point Clouds in Autonomous Driving Systems
Motivated by this potential, this paper focuses on sim-to-real mapping of Lidar point clouds, a widely used perception sensor in automated driving systems.
Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labels
Due to the costliness of labelled data in real-world applications, semi-supervised learning, underpinned by pseudo labelling, is an appealing solution.
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement.
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems.
Semi-supervised Object Detection via Virtual Category Learning
Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing.
Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video?
Recently, Deep Learning-based methods for inverse tone-mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular.
Deep HDR Hallucination for Inverse Tone Mapping
Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content.
Deep Controllable Backlight Dimming
Dual-panel displays require local dimming algorithms in order to reproduce content with high fidelity and high dynamic range.
Spectrally Consistent UNet for High Fidelity Image Transformations
Convolutional Neural Networks (CNNs) are the current de-facto models used for many imaging tasks due to their high learning capacity as well as their architectural qualities.
Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments
Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner.
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet.
