MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation
Wasim, S. T., Suleman, H., Zatsarynna, O., Naseer, M., & Gall, J. (2025). MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2509.11394
MaskTerial: A Foundation Model for Automated 2D Material Flake Detection
Uslu, J.-L., Nekrasov, A., Hermans, A., Beschoten, B., Leibe, B., Waldecker, L., & Stampfer, C. (2024). MaskTerial: A Foundation Model for Automated 2D Material Flake Detection. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2412.09333
Sa2VA-i: Improving Sa2VA Results with Consistent Training and Inference
Holland, L. V., Kaspers, N., Dengler, N., Stotko, P., Bennewitz, M., & Klein, R. (2025). Towards Rhino-AR: A System for Real-Time 3D Human Pose Estimation and Volumetric Scene Integration on Embedded AR Headsets. In 2025 11th International Conference on Virtual Reality (ICVR) (pp. 135–143). 2025 11th International Conference on Virtual Reality (ICVR). IEEE. https://doi.org/10.1109/icvr66534.2025.11172603
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction
Heidrich, S., Beemelmanns, T., Nekrasov, A., Leibe, B., & Eckstein, L. (2025). OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2503.10605
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
Nekrasov, A., Burdorf, M., Worrall, S., Leibe, B., & Berrio, J. (2025). Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving. doi:10.48550/arXiv.2505.02148
Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions
Tseng, T.-Y., Nekrasov, A., Burdorf, M., Leibe, B., Berrio, J. S., Shan, M., … Worrall, S. (2025). Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2503.16378
Exploring the Potential of Retrieval Augmented Generation for Question Answering in Radiology: Initial Findings and Future Directions
Mou, Y., Siepmann, R. M., Truhnn, D., Sowe, S., & Decker, S. (2025). Exploring the Potential of Retrieval Augmented Generation for Question Answering in Radiology: Initial Findings and Future Directions. Studies in health technology and informatics, 327, 863–867. https://doi.org/10.3233/SHTI250482
WILLM: A System for Academic Writing Improvement Based on Large Language Models
Mou, Y., Fathi, F., Thillen, B., & Decker, S. (2025). WILLM: A system for academic writing improvement based on large language models. In A. I. Cristea, E. Walker, Y. Lu, O. C. Santos, & S. Isotani (Eds.), Artificial intelligence in education (AIED 2025, Lecture Notes in Computer Science, Vol. 15881). Springer. https://doi.org/10.1007/978-3-031-98462-4_5
What Can We Learn From MIMO Graph Convolutions?
Roth, A., & Liebig, T. (2025). What Can We Learn From MIMO Graph Convolutions? arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2505.11346
Expressive Pooling for Graph Neural Networks
Lachi, V., Moallemy-Oureh, A., Roth, A., & Welke, P. (2025). Expressive Pooling for Graph Neural Networks. Transactions on Machine Learning Research. Retrieved from https://openreview.net/forum?id=xGADInGWMt
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