Multimodal representation learning is gaining more and more interest within the deep
learning community. While bilinear models provide an interesting framework to find subtle …

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 …

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 …

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 …

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 …

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 …

In this paper, we introduce a new model for leveraging unlabeled data to improve
generalization performances of image classifiers: a two-branch encoder-decoder …

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 …

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 …

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 …