WebA large number of accurate annotations of targets is a prerequisite for efficient and accurate object detection. However, to obtain such annotated samples for completing detection model training is time-consuming, laborious, and difficult to achieve. Usually, the training samples often contain noisy annotation, including mislabeled class and inaccurate … WebJul 13, 2024 · Object detection, as a fundamental computer vision task, has achieved a remarkable progress with the emergence of deep neural networks. Nevertheless, few works explore the adversarial robustness of object detectors to resist adversarial attacks for practical applications in various real-world scenarios.
多模态最新论文分享 2024.4.11 - 知乎 - 知乎专栏
WebApr 11, 2024 · The existing zero-shot OOD detection setting does not consider the realistic case where an image has both in-distribution (ID) objects and OOD objects. However, it is important to identify such images as ID images when collecting the images of rare classes or ethically inappropriate classes that must not be missed. WebRobust Object Detection With Inaccurate Bounding Boxes 3 learn object detectors. Different from previous work, we focus on object detec-tion with noisy bounding box annotations. … alethia pro font
A Robust Learning Approach to Domain Adaptive Object …
WebJul 20, 2024 · As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the … WebApr 12, 2024 · Towards Robust Tampered Text Detection in Document Image: New dataset and New Solution Chenfan Qu · Chongyu Liu · Yuliang Liu · Xinhong Chen · Dezhi Peng · … WebJul 20, 2024 · As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data. In this work, we aim to address the challenge of learning robust object detectors with inaccurate bounding boxes. alethia pierson