3 days ago · This systematic review deconstructs object detection research's evolution, methodology, and challenges by integrating evidence from high-impact repositories. Publication trends, dataset usage, and domination of leading venues like CVPR and ICCV in driving the field are addressed. Comparative performance assessment of YOLO, Faster R-CNN, and DETR considers their performance, scalability, and ...

Nov 20, 2020 · We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost.

Nov 1, 2004 · In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates.

All object recognition systems use models either explicitly or implicitly and employ feature detectors based on these object models. The hypothesis formation and verification components vary in their importance in different approaches to object recognition. Some systems use only hypothesis forma­

Jun 23, 2021 · Object detection and recognition is the problem of localizing an object in the image and classifying it into a set of predefined object categories or specific object instances. Object detection and recognition is one of the central problems in computer vision and robot perception.

3 days ago · Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem ...

1 day ago · Single-source Domain Generalization (SDG) in object detection aims to develop a detector using only data from a source domain that can exhibit strong generalization capability when applied to unseen target domains. Existing methods are built upon CNN-based detectors and primarily improve robustness by employing carefully designed data augmentation strategies integrated with feature alignment ...

Aug 13, 2024 · Object recognition is a fascinating field within computer vision where you teach a computer to identify and classify objects within images or video streams. Imagine you’re looking at a photo,...

5 days ago · We begin by outlining the unique challenges of 3D object detection with vision-language models, emphasizing differences from 2D detection in spatial reasoning and data complexity. Traditional approaches using point clouds and voxel grids are compared to modern vision-language frameworks like CLIP and 3D LLMs, which enable open-vocabulary ...

In recent years, with the rapid development of deep learning techniques, object detection algorithms such as Faster R-CNN [1] and YOLO [2] have made significant progress. This is particularly important for tasks such as text recognition [3], anomaly detection [4], object detection [5], and image classification [6].However, these achievements are highly dependent on the availability of large ...