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Found 7 papers, 5 papers with code
Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks
Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong.
Parameter-free Online Test-time Adaptation
An interesting and practical paradigm is online test-time adaptation, according to which training data is inaccessible, no labelled data from the test distribution is available, and adaptation can only happen at test time and on a handful of samples.
Query-based Hard-Image Retrieval for Object Detection at Test Time
There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory.
Attacking deep networks with surrogate-based adversarial black-box methods is easy
A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search.
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
This enables active learning Gaussian process methods to be applied to problems where the performance of the system is sometimes undefined, and we demonstrate the effectiveness of our approach by testing our methodology on synthetic numerical examples for the autonomous driving domain.
Attacking Motion Planners Using Adversarial Perception Errors
Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics.
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers
Combining complementary sensor modalities is crucial to providing robust perception for safety-critical robotics applications such as autonomous driving (AD).
