News-Archiv

The paper "Data-Efficient Spectral Classification of Hyperspectral Data UsingMiniROCKET and HDC-MiniROCKET" by Nick Theisen, Kenny Schlegel , Dietrich Paulus, and Peer Neubert ash been accepted at the CASE 2025 conference.

Abstract - The classification of pixel spectra of hyperspectralimages, i. e. spectral classification, is used in many fieldsranging from agricultural, over medical to remote sensing applications and is currently also expanding to areas such asautonomous driving. Even though for full hyperspectral images the best-performing methods exploit spatial-spectral information, performing classification solely on spectral informationhas its own advantages, e. g. smaller model size and thus less data required for training. Moreover, spectral information is complementary to spatial information and improvements on either part can be used to improve spatial-spectral approaches in the future. Recently, 1D-Justo-LiuNet was proposed as a particularly efficient model with very few parameters, which currently defines the state of the art in spectral classification. However, we show that with limited training data the model performance deteriorates. Therefore, we investigate MiniROCKETand HDC-MiniROCKET for spectral classification to mitigatethat problem. The model extracts well-engineered features without trainable parameters in the feature extraction part and is therefore less vulnerable to limited training data. We show that even though MiniROCKET has more parameters it outperforms 1D-Justo-LiuNet in limited data scenarios and ismostly on par with it in the general case.

After the successful start last year, we will organize another CV-Camp in 2025: https://uni-ko.de/cv-camp

The paper "On the choice of Vector Symbolic Architectures for Time Series Classification with HDC-MiniROCKET" by Marcel Unger , Kenny Schlegel , Peter Protzel , and Peer Neubert has bee accepted at the IJCNN 2025 conference.

Abstract—Hyperdimensional Computing (HDC) has shown promise in time series classification by enhancing MiniROCKET, forming HDC-MiniROCKET. However, the impact of choosing a specific HDC implementation, referred to as a Vector Symbolic Architecture (VSA), within this framework is so far unexplored. This paper systematically evaluates different VSAs within HDC-MiniROCKET, analyzing their impact on classification performance, hyperparameter sensitivity, and computational efficiency. Our findings reveal that certain VSAs require a significantly broader range of hyperparameter values to achieve optimal accuracy despite similar properties. We demonstrate this effect on a synthetic dataset and validate it across a subset of the real-world benchmark UCR, showing that VSA performance varies depending on the dataset characteristics. Additionally, we investigate the trade-off between computational complexity and classification accuracy. We find that computationally efficient VSAs not only reduce processing time but also achieve comparable or superior accuracy to more complex alternatives. These insights help to select VSAs in HDC-based time series classification.

The paper "Towards Zero-Shot Terrain Traversability Estimation: Challenges and Opportunities" by Ida Germann, Mark O. Mints and Peer Neubert has been accepted at the 1st German Robotics Conference 2025.

Abstract— Terrain traversability estimation is crucial forau tonomous robots, especially in unstructured environments where visual cues and reasoning play a key role. While vision-language models (VLMs) offer potential for zero-shot estimation, the problem of traversability classification remains inherently ill-posed. To explore this, we introduce a small dataset of human-annotated water traversability ratings, revealing that while estimations are subjective, human raters still show some consensus. Additionally, we propose a simple pipeline that integrates VLMs for zero-shot traversability estimation. Our experiments reveal mixed results, suggesting that current foundation models are not yet suitable for practical deployment but provide valuable insights for further research.

Nick works is expert for semantic segmentation and hyperspectal images. He will work in the MineSweeper project.

This new 3-year research project is on visual navigation in challenging and changing environments in the context of civil demining

Congratulations to Jessica Buchner for winning the 1st place jury prize at CV Day for her work "Visual Tracking of Reptiles in a Zoological Context" and to Felix Meyer for winning the 1st place audience award for his work "Camera-based overtaking distance control"!

The Paper "HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios" by Nick Theisen, Robin Bartsch, Dietrich Paulus, and Peer Neubert has been accepted at IROS'24.

Abstract: Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology enables the application in driving scenarios and the hope is that the device's perceptive abilities provide an advantage over RGB-cameras. Even though some datasets exist,there is no standard benchmark available to systematically measure progress on this task and evaluate the benefit of hyperspectral data. In this paper, we work towards closing this gap by providing the HyperSpectral Semantic Segmentation benchmark (HS3-Bench). It combines annotated hyperspectral images from three driving scenario datasets and provides standardized metrics, implementations, and evaluation protocols. We use the benchmark to derive two strong baseline models that surpass the previous state-of-the-art performances with and without pre-training on the individual datasets. Further, our results indicate that the existing learning-based methods benefit more from leveraging additional RGB training data than from leveraging the additional hyperspectral channels. This poses important questions for future research on hyperspectral imaging for semantic segmentation in driving scenarios.

The paper "Towards Revisiting Visual Place Recognition for Joining Submaps in Multimap SLAM" by Markus Weißflog, Stefan Schubert, Peter Protzel, and Peer Neubert has been accepted at TAROS'24.

Abstract: Visual SLAM is a key technology for many autonomous systems. However, tracking loss can lead to the creation of disjoint submaps in multimap SLAM systems like ORB-SLAM3. Because of that, these systems employ submap merging strategies. As we show, these strategies are not always successful. In this paper, we investigate the impact of using modern VPR approaches for submap merging in visual SLAM. We argue that classical evaluation metrics are not sufficient to estimate the impact of a modern VPR component on the overall system. We show that naively replacing the VPR component does not leverage its full potential without requiring substantial interference in the original system. Because of that, we present a post-processing pipeline along with a set of metrics that allow us to estimate the impact of modern VPR components. We evaluate our approach on the NCLT and Newer College datasets using ORB-SLAM3 with NetVLAD and HDC-DELF as VPR components. Additionally, we present a simple approach for combining VPR with temporal consistency for map merging. We show that the map merging performance of ORB-SLAM3 can be improved. Building on these results, researchers in VPR can assess the potential of their approaches for SLAM systems.

Preprint: https://arxiv.org/abs/2407.12408

She will work in the project "AI-DPA: Analyse und Interpretation von unstrukturierten Daten und Prozessen in zwei- und dreidimensionalen Anwendungsszenarien mit Machine Learning"

Although I already started in Koblenz in 2022, I will now give my inaugural lecture with the title "Künstliche Intelligenz hat viele Dimensionen: Die Magie hochdimensionaler Vektorrepräsentationen für Roboter und andere autonome Systeme" (the lecture will be in German).

Anyone interested is warmly invited!

February 1, 18:00-20:00, Room M001, University Campus

A joint team from our group and researchers from Sweden and Australia has won the "My Seizure Gauge'' Challenge on forecasting epileptic seizures from non-cerebral signals. A short summary of the competition and our winning approach is provided in a paper that has been accepted for publication in Nature Machine Intelligence in the Challenge Accepted track. Congratulations to Kenny Schlegel who has led this team! The paper is a joint work of our team and the challenge organizers.

Kenny Schlegel, Denis Kleyko, Benjamin H. Brinkmann, Ewan S. Nurse, Ross W. Gayler, Peer Neubert (2024). Lessons from the “My Seizure Gauge” Challenge on Forecasting Epileptic Seizures from Non-Cerebral Signals. Nature Machine Intelligence Challenge Accepted (to appear)

The paper "Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline" by Fangming Yuan, Stefan Schubert, Peter Protzel, and Peer Neubert has been accepted for publication in the IEEE RA-L journal.

Abstract: Large-scale applications of Visual Place Recognition (VPR) require computationally efficient approaches. Further, a well-balanced combination of data-based and training-free approaches can decrease the required amount of training data and effort and can reduce the influence of distribution shifts between the training and application phases. This paper proposes a runtime and data-efficient hierarchical VPR pipeline that extends existing approaches and presents novel ideas. There are three main contributions: First, we propose Local Positional Graphs (LPG), a training-free and runtime-efficient approach to encode spatial context information of local image features. LPG can be combined with existing local feature detectors and descriptors and considerably improves the image-matching quality compared to existing techniques in our experiments. Second, we present Attentive Local SPED (ATLAS), an extension of our previous local features approach with an attention module that improves the feature quality while maintaining high data efficiency. The influence of the proposed modifications is evaluated in an extensive ablation study. Third, we present a hierarchical pipeline that exploits hyperdimensional computing to use the same local features as holistic HDC-descriptors for fast candidate selection and for candidate reranking. We combine all contributions in a runtime and data-efficient VPR pipeline that shows benefits over the state-of-the-art method Patch-NetVLAD on a large collection of standard place recognition datasets with 15x better performance in VPR accuracy, 54x faster feature comparison speed, and 27x less descriptor storage occupancy, making our method promising for real-world high-performance large-scale VPR in changing environments. Code will be made available with publication of this paper.