Cascade R-CNN: Delving Into High Quality Object Detection
In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usua...
Publications
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226 publications
Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction
BACKGROUND In patients with type 2 diabetes, inhibitors of sodium-glucose cotransporter 2 (SGLT2) reduce the risk of a first hospitalization for heart failure, possibly through ...
Dynamic Graph CNN for Learning on Point Clouds
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition ...
Image Style Transfer Using Convolutional Neural Networks
Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack ...
A ConvNet for the 2020s
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification...
FCOS: Fully Convolutional One-Stage Object Detection
We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all stat...
Content Analysis: An Introduction to Its Methodology
"The Second Edition of Content Analysis is a sourcebook of the history and core principles of content analysis as well as an essential resource for present and future studies. T...
Active appearance models
We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a trainin...
Deep High-Resolution Representation Learning for Human Pose Estimation
In this paper, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resol...
Image Super-Resolution Via Sparse Representation
This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-...