GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …
spatial analytics in Geography. Although much progress has been made in exploring the …
Region proposal by guided anchoring
Region anchors are the cornerstone of modern object detection techniques. State-of-the-art
detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly …
detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly …
Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation
Skeleton-based human action recognition has recently drawn increasing attentions with the
availability of large-scale skeleton datasets. The most crucial factors for this task lie in two …
availability of large-scale skeleton datasets. The most crucial factors for this task lie in two …
Detection in crowded scenes: One proposal, multiple predictions
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-
overlapped instances in crowded scenes. The key of our approach is to let each proposal …
overlapped instances in crowded scenes. The key of our approach is to let each proposal …
Swapmix: Diagnosing and regularizing the over-reliance on visual context in visual question answering
Abstract While Visual Question Answering (VQA) has progressed rapidly, previous works
raise concerns about robustness of current VQA models. In this work, we study the …
raise concerns about robustness of current VQA models. In this work, we study the …
Cascade rpn: Delving into high-quality region proposal network with adaptive convolution
This paper considers an architecture referred to as Cascade Region Proposal Network
(Cascade RPN) for improving the region-proposal quality and detection performance by …
(Cascade RPN) for improving the region-proposal quality and detection performance by …
Cascade object detection and remote sensing object detection method based on trainable activation function
Object detection is an important process in surveillance system to locate objects and it is
considered as major application in computer vision. The Convolution Neural Network (CNN) …
considered as major application in computer vision. The Convolution Neural Network (CNN) …
STDnet: Exploiting high resolution feature maps for small object detection
The accuracy of small object detection with convolutional neural networks (ConvNets) lags
behind that of larger objects. This can be observed in popular contests like MS COCO. This …
behind that of larger objects. This can be observed in popular contests like MS COCO. This …
Generalizing state-of-the-art object detectors for autonomous vehicles in unseen environments
In scene understanding for autonomous vehicles (AVs), models trained on the available
datasets fail to generalize well to the complex, real-world scenarios with higher dynamics. In …
datasets fail to generalize well to the complex, real-world scenarios with higher dynamics. In …
Cascade RetinaNet: Maintaining consistency for single-stage object detection
Recent researches attempt to improve the detection performance by adopting the idea of
cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency …
cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency …