Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training
نویسندگان
چکیده
منابع مشابه
SIDF: A Novel Framework for Accurate Surgical Instrument Detection in Laparoscopic Video Frames
Background and Objectives: Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for a...
متن کاملVideo game training and the reward system
Video games contain elaborate reinforcement and reward schedules that have the potential to maximize motivation. Neuroimaging studies suggest that video games might have an influence on the reward system. However, it is not clear whether reward-related properties represent a precondition, which biases an individual toward playing video games, or if these changes are the result of playing video ...
متن کاملShape Training for Video Object Segmentation
Since most algorithms for automatic video segmentation cannot extract video objects in a picture frame accurately, we can take a user-assisted approach in generating VOPs of moving objects. In this paper, we propose a semiautomatic video segmentation algorithm using semantic information. In order to reduce effects of unwanted feature points due to the low-level image processing operations, we e...
متن کاملVideo Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics
Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers...
متن کاملTraining-free, Generic Object Detection using Locally Adaptive Regression Kernels
We present a generic detection/localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches; does not require prior knowledge (learning) about objects being sought; and does not require any pre-processing step or segmentation of a target image. Our method is base...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3006191