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Found 15 papers, 7 papers with code
FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation
In the realm of fashion object detection and segmentation for online shopping images, existing state-of-the-art fashion parsing models encounter limitations, particularly when exposed to non-model-worn apparel and close-up shots.
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes
In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis.
Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals
We present a human-in-the-loop dashboard tailored to diagnosing potential spurious features that NLI models rely on for predictions.
LU-Net: Invertible Neural Networks Based on Matrix Factorization
Also, the computation of the determinant of the Jacobian matrix of such layers is cheap.
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects.
What should AI see? Using the Public's Opinion to Determine the Perception of an AI
Finally, we include comments from industry leaders in the field of AI safety on the applicability of survey based elements in the design of AI functionalities in automated driving.
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
Training deep neural networks is already resource demanding and so is also their uncertainty quantification.
Detecting and Learning the Unknown in Semantic Segmentation
However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to.
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes.
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation
In our experiments we consistently observe a clear additional gain in OoD detection performance, cutting down the number of detection errors by up to 52% when comparing the best baseline with our results.
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation
We present proof-of-concept results for CIFAR-10, and prove the efficiency of our method for the semantic segmentation of street scenes on the Cityscapes dataset based on predicted instances of the 'human' class.
Detection of False Positive and False Negative Samples in Semantic Segmentation
In recent years, deep learning methods have outperformed other methods in image recognition.
The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation
Neural networks for semantic segmentation can be seen as statistical models that provide for each pixel of one image a probability distribution on predefined classes.
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
We approach such potential misclassifications by weighting the posterior class probabilities with the prior class probabilities which in our case are the inverse frequencies of the corresponding classes in the training dataset.
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities
We aggregate these dispersion measures segment-wise and derive metrics that are well-correlated with the segment-wise IoU of prediction and ground truth.
