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Found 13 papers, 6 papers with code
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios.
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time
We find that DESOT obtains the benefits of deep ensembles, in terms of predictive and uncertainty estimation performance, while avoiding the added computational cost.
Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty.
Raw or Cooked? Object Detection on RAW Images
Images fed to a deep neural network have in general undergone several handcrafted image signal processing (ISP) operations, all of which have been optimized to produce visually pleasing images.
Towards Explainable Motion Prediction using Heterogeneous Graph Representations
In this work, we aim to improve the explainability of motion prediction systems by using different approaches.
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
First, we comprehensively analyse the evaluation metrics, identify the main gaps of current benchmarks, and propose a new holistic evaluation framework.
Future Object Detection with Spatiotemporal Transformers
In order to make accurate predictions about the future, it is necessary to capture the dynamics in the scene, both object motion and the movement of the ego-camera.
Dense Gaussian Processes for Few-Shot Segmentation
Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions.
Ranked #1 on
Few-Shot Semantic Segmentation
on COCO-20i (10-shot)
Deep Gaussian Processes for Few-Shot Segmentation
Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space.
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output.
A Generative Appearance Model for End-to-end Video Object Segmentation
One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance.
Unveiling the Power of Deep Tracking
In the field of generic object tracking numerous attempts have been made to exploit deep features.
DCCO: Towards Deformable Continuous Convolution Operators
Generally, DCF based trackers learn a rigid appearance model of the target.
