Detecting objects remains one of computer vision and image understanding applications' most fundamental and challenging aspects. Significant advances in object detection have …
CY Wang, IH Yeh, HY Mark Liao - European conference on computer …, 2024 - Springer
Today's deep learning methods focus on how to design the objective functions to make the prediction as close as possible to the target. Meanwhile, an appropriate neural network …
We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the training stage, object boxes …
C Li, L Li, H Jiang, K Weng, Y Geng, L Li, Z Ke… - arXiv preprint arXiv …, 2022 - arxiv.org
For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a …
CY Wang, A Bochkovskiy… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are …
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance …
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are …
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a …
One-to-one label assignment in object detection has successfully obviated the need of non- maximum suppression (NMS) as a postprocessing and makes the pipeline end-to-end …