A comprehensive review of recent advances on deep vision systems
Real-time video objects detection, tracking, and recognition are challenging issues due to
the real-time processing requirements of the machine learning algorithms. In recent years …
the real-time processing requirements of the machine learning algorithms. In recent years …
The limits and potentials of deep learning for robotics
The application of deep learning in robotics leads to very specific problems and research
questions that are typically not addressed by the computer vision and machine learning …
questions that are typically not addressed by the computer vision and machine learning …
Active object detection with multistep action prediction using deep q-network
In recent years, great success has been achieved in visual object detection, which is one of
the fundamental tasks in the field of industrial intelligence. Most of existing methods have …
the fundamental tasks in the field of industrial intelligence. Most of existing methods have …
Multi-shot pedestrian re-identification via sequential decision making
Multi-shot pedestrian re-identification problem is at the core of surveillance video analysis. It
matches two tracks of pedestrians from different cameras. In contrary to existing works that …
matches two tracks of pedestrians from different cameras. In contrary to existing works that …
Particle swarm optimization based swarm intelligence for active learning improvement: Application on medical data classification
Semi-supervised learning targets the common situation where labeled data are scarce but
unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In …
unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In …
Deep reinforcement learning attention selection for person re-identification
Existing person re-identification (re-id) methods assume the provision of accurately cropped
person bounding boxes with minimum background noise, mostly by manually cropping. This …
person bounding boxes with minimum background noise, mostly by manually cropping. This …
Recurrent models of visual co-attention for person re-identification
L Lin, H Luo, R Huang, M Ye - IEEE access, 2019 - ieeexplore.ieee.org
Person re-identification (re-id) refers to matching people across disjoint camera views. Most
of person re-id methods extract discriminative features from the whole images or fixed …
of person re-id methods extract discriminative features from the whole images or fixed …
Active vision in the era of convolutional neural networks
D Gallos, F Ferrie - 2019 16th Conference on Computer and …, 2019 - ieeexplore.ieee.org
In this work, we examine the literature of active object recognition in the past and present.
We note that methods in the past used a notion of recognition ambiguity in order to find a …
We note that methods in the past used a notion of recognition ambiguity in order to find a …
Flar: A unified prototype framework for few-sample lifelong active recognition
Intelligent agents with visual sensors are allowed to actively explore their observations for
better recognition performance. This task is referred to as Active Recognition (AR). Currently …
better recognition performance. This task is referred to as Active Recognition (AR). Currently …
Towards efficient multiview object detection with adaptive action prediction
Active vision is a desirable perceptual feature for robots. Existing approaches usually make
strong assumptions about the task and environment, thus are less robust and efficient. This …
strong assumptions about the task and environment, thus are less robust and efficient. This …