arXiv Computer Vision
Annotator-Free Object Detection via Foundation Model Collaboration
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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
Oct 22
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 2
GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).
arXiv Computer Vision
Oct 5
OpenBox: Annotate Any Bounding Boxes in 3D
OpenBox is a two‑stage automatic annotation pipeline that uses a 2D vision foundation model to associate 2D image cues with 3D point clouds. It then classifies instances by rigidity and motion state to generate adaptive bounding boxes using class‑specific size statistics, eliminating the need for self‑training. Experiments on Waymo Open Dataset, Lyft Level 5 Perception, and nuScenes show improved accuracy and efficiency over existing baselines.
arXiv Computer Vision
Sep 1
FoundYou: A Unified Model for Personalized Segmentation and Retrieval
arXiv:2608.29917v1 Announce Type: new Abstract: Personalized segmentation and personalized retrieval both aim to identify the same physical object across different images. While the former localizes...
arXiv Computer Vision
Oct 23
WeVisOne-VL: Unified Visual Perception from Objects to Pixels
WeVisOne‑VL is a new vision‑language model that unifies object‑level and pixel‑level perception through structured next‑token prediction. It supports a wide range of visual tasks—object detection, referring localization, counting, OCR, segmentation, and matting—allowing localization at any user‑specified granularity from points and boxes to masks and alpha mattes. The architecture incorporates a lightweight pixel decoder via a special token, and the model is trained with a three‑stage curriculum followed by mixed‑task reinforcement learning, achieving leading performance on over 30 benchmarks while robustly rejecting object‑absent queries.