# Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks Source: [Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:00:40.638Z Authors: Laura Fieback, Bidya Dash, Jakob Spiegelberg, Hanno Gottschalk (2) \_\_INSTITUTION\_4\_\_ Volkswagen AG, (2) TU Berlin) Published: 2023-11-13T17:11:35.000Z ## Abstract Quantifying predictive uncertainty of deep semantic segmentation networks is essential in safety-critical tasks. In applications like autonomous driving, where video data is available, convolutional long short-term memory networks are capable of not only providing semantic segmentations but also predicting the segmentations of the next timesteps. These models use cell states to broadcast information from previous data by taking a time series of inputs to predict one or even further steps into the future. We present a temporal postprocessing method which estimates the prediction performance of convolutional long short-term memory networks by either predicting the intersection over union of predicted and ground truth segments or classifying between intersection over union being equal to zero or greater than zero. To this end, we create temporal cell state-based input metrics per segment and investigate different models for the estimation of the predictive quality based on these metrics. We further study the influence of the number of considered cell states for the proposed metrics. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()