arXiv Computer Vision
Quantifying Annotation Agreement in Object Detection with Optimal Transport
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 21
Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
The paper demonstrates that object detection benchmarks suffer from incomplete annotations, with re-annotation of COCO, Pascal VOC, Cityscapes, and KITTI revealing up to a 60% increase in detected objects, especially small, occluded, or densely packed instances. The authors propose a scalable annotation pipeline that uses multiple annotators per object to capture uncertainty and improve recall, and they introduce two new large-scale benchmarks: an uncertainty-aware detection benchmark and a label error detection benchmark based on real errors. Their findings show that benchmark performance is highly sensitive to annotation quality, yet model rankings remain largely unchanged, highlighting the need for uncertainty-aware evaluation to better reflect real-world ambiguity.
arXiv Computer Vision
3d ago
Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets
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.
arXiv AI
Jun 11
SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.
Hugging Face Trending Papers
Sep 3
Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language
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, enabling attribution of differences to specific objects or categories. A new benchmark, AD‑Diff Bench, is presented to evaluate this method, especially for sparse, real‑world differences, and the authors provide open‑weight models and code for reproducibility.
arXiv Machine Learning
Sep 4
Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language
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.
arXiv Computer Vision
Sep 24
ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus
arXiv:2609.28239v1 Announce Type: new Abstract: With the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulne...