The paper introduces set difference captioning for autonomous driving datasets, aiming to generate natural‑language descriptions of differences between two image subsets. It adapts a two‑stage approach to focus on object‑centric patches, allowing attribution of differences to specific objects or categories. A new benchmark, AD‑Diff Bench, is presented to evaluate these methods, especially for sparse, real‑world differences, with open‑weight models to ensure reproducibility.
By Julian Truetsch, Felix Hauser, Christoph Stiller, Frank Bieder
Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban infrastructure monitoring. Prior work addresses it...
arXiv:2609.10356v1 Announce Type: new
Abstract: Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban i...
By Benedetta Liberatori, Nermin Samet, Paolo Rota, Matthieu Cord, Elisa Ricci, Andrei Bursuc, Monika Wysocza\'nska
arXiv:2606. 09362v1 Announce Type: cross Abstract: Re-Identification (ReID) in autonomous driving is typically formulated as a visual matching problem, where observations of vehicles, pedestrians, and cyclists are associated across time, frames, or camera views using learned appearance embeddings, often complemented by motion, geometric, or multimodal cues.
By Eduardo Borges, Manuel Abreu, Lu\'is Garrote, Urbano J. Nunes
arXiv:2410. 21361v2 Announce Type: replace-cross Abstract: Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in real-world autonomous driving scenarios, especially under rare or adverse conditions.
By Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick P\'erez, Raoul de Charette
arXiv:2504.18190v2 Announce Type: replace
Abstract: Unsupervised Domain Adaptation (UDA) can improve a perception model's generalization to an unlabeled target domain starting from a labeled source d...
By Brun\'o B. Englert, Tommie Kerssies, Gijs Dubbelman
arXiv:2507. 19881v2 Announce Type: replace-cross Abstract: Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data.
By Tao Lian, Jose L. G\'omez, Antonio M. L\'opez
Semantically-Guided Domain Randomization (S‑GDR) is an annotation‑free pipeline that uses vision‑language model captioning of a small real reference set, diffusion‑based background synthesis, and mask‑based object composition to generate synthetic training data. In a high‑mix, low‑volume automotive detection benchmark, S‑GDR achieves a mAP50‑95 of 0.739 with only 200 synthetic images, outperforming a domain‑randomized render baseline and several other synthetic data methods under the same budget. These results suggest S‑GDR is a viable alternative for training visual perception systems when annotation, energy, and time resources are severely limited.
By Jose Moises Araya-Martinez, Gautham Mohan, Jens Lambrecht
The paper presents a semi‑supervised framework that uses open‑vocabulary object detectors to generate region proposals and semantic representations, builds an object‑level similarity graph, and propagates probabilistic labels to assign soft, uncertainty‑aware annotations to previously unlabeled objects. Experiments on four detection benchmarks show that high‑quality soft labels can be produced with only a small fraction of manual annotations, and models trained on these semi‑automatically annotated datasets achieve performance comparable to fully manually annotated models. On COCO, the method reaches 40.5% AP, nearly matching the 40.9% AP of a fully annotated model while incurring only 1% of the labeling cost.
By Jonathan Klees, Timo Heller, Matthias Rottmann
NeuroSymbEAD is a large‑scale neuro‑symbolic caption dataset that builds an ego‑centric knowledge graph of static and dynamic objects on the KITTI‑360 dataset, annotating classes, categories, heading directions, orientations, and distances from the ego‑vehicle. The dataset generates multilevel textual captions that serve as a lightweight representation of an ego‑centric scene map, enabling outdoor scene‑map reconstruction, visual recognition, and object grounding. Baselines for driving common sense and traffic/scene understanding are established, and the dataset is benchmarked using pre‑trained grounding and learned auto‑regressive captioning networks to support vision‑language and foundation models for traffic‑scene explanation, 3D reasoning, and interpretable autonomous‑driving perception.
By Muhammad Ahmed Ullah Khan, Mohammed Elamine, Sheikh Talha Uddin, Didier Stricker, Sk Aziz Ali, Muhammad Zeshan Afzal
arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.
By Nikolai R\"ohrich, Julian Glei{\ss}ner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber
Retraining visual perception pipelines in High-Mix, Low-Volume (HMLV) automotive manufacturing must be carried out under tight annotation, energy, and time budgets, yet most Synthetic Data Generation...