1.1 Comparison with Previous Reviews 之前有以下几个方面的综述 generic object detection detectors designed for several specific objects imbalance problems existing in deep-neural-network based detectors few-shot learning meta-learning deep-neural-network architectures specific applications of few-shot learning...
Object detection algorithms encounter various challenges in the form of transformations and ambient distortions(like blur, noise, illumination) which lead to significant amount of failures in successful object recognition. This paper discusses the factors involved in developing invariance with regards to ...
from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the ...
Object detection, by comparison, delineates individual objects in an image according to specified categories. While image classification divides images among those that have stop signs and those that do not, object detection locates and categorizes all of the road signs in an image, as well as ...
【笔记】Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to La,程序员大本营,技术文章内容聚合第一站。
augmented the Yolov5 with an additional small object detection head, which enhanced the precision for such objects. However, the resultant computational complexity was significantly high, leading to slower detection speeds. Aimed at the practical application of object detection algorithms in UAV scenarios...
Figure 1. Comparison between the top 10 performer of VisDrone- DET2019 (blue) and VisDrone-DET2018 (red). Small object detection. Objects are usually very small in drone based scenes. As shown in Figure 2, DPNet- ensemble (A.15) performs wel...
Most popular metrics used to evaluate object detection algorithms. - GitHub - jutianyin/Object-Detection-Metrics: Most popular metrics used to evaluate object detection algorithms.
Despite the recent growth and proliferation of Machine Learning (ML) object detection algorithms, most approaches commonly focus on the visible light portion of the electromagnetic spectrum, for example, using Red–Green–Blue (RGB) images1,2,3,4. Hitherto, thermal Long Wave Infrared (LWIR) spect...
turn out to be more favorable than many prior works that use meta-learning on few-shot image clas-sification . As for the emerging few-shot object detection task, there is neither consensus on the evaluation benchmarks nor a consistent comparison of different approaches due to the increased ...