Block: Bilinear superdiagonal fusion for visual question answering and visual relationship detection
Multimodal representation learning is gaining more and more interest within the deep
learning community. While bilinear models provide an interesting framework to find subtle …
learning community. While bilinear models provide an interesting framework to find subtle …
Learning region features for object detection
While most steps in the modern object detection methods are learnable, the region feature
extraction step remains largely hand-crafted, featured by RoI pooling methods. This work …
extraction step remains largely hand-crafted, featured by RoI pooling methods. This work …
Robust appearance modeling for object detection and tracking: a survey of deep learning approaches
A Mumuni, F Mumuni - Progress in Artificial Intelligence, 2022 - Springer
The task of object detection and tracking is one of the most complex and challenging
problems in artificial intelligence (AI) systems that model perception. Object tracking has …
problems in artificial intelligence (AI) systems that model perception. Object tracking has …
An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks
D Cao, Z Chen, L Gao - Human-centric Computing and Information …, 2020 - Springer
Object detection methods aim to identify all target objects in the target image and determine
the categories and position information in order to achieve machine vision understanding …
the categories and position information in order to achieve machine vision understanding …
Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms
Purpose The practice of artificial intelligence (AI) is increasingly being promoted by
technology developers. However, its adoption rate is still reported as low in the construction …
technology developers. However, its adoption rate is still reported as low in the construction …
Deep regionlets for object detection
In this paper, we propose a novel object detection framework named" Deep Regionlets" by
establishing a bridge between deep neural networks and conventional detection schema for …
establishing a bridge between deep neural networks and conventional detection schema for …
Hybridnet: Classification and reconstruction cooperation for semi-supervised learning
In this paper, we introduce a new model for leveraging unlabeled data to improve
generalization performances of image classifiers: a two-branch encoder-decoder …
generalization performances of image classifiers: a two-branch encoder-decoder …
Deformable template network (DTN) for object detection
Objects often have different appearances because of viewpoint changes or part deformation.
How to reasonably model these variations is still a big challenge for object detection. In this …
How to reasonably model these variations is still a big challenge for object detection. In this …
End-to-end learning of latent deformable part-based representations for object detection
Object detection methods usually represent objects through rectangular bounding boxes
from which they extract features, regardless of their actual shapes. In this paper, we apply …
from which they extract features, regardless of their actual shapes. In this paper, we apply …
DSN: A new deformable subnetwork for object detection
S Wu, Y Xu - IEEE Transactions on Circuits and Systems for …, 2019 - ieeexplore.ieee.org
Although deep convolutional neural networks have achieved great success in object
detection, they depend heavily on considerable training data and do not have a specific …
detection, they depend heavily on considerable training data and do not have a specific …