1 citations · 3 across the 6 of their papers we have counts for
6 papers
Few-shot Object Detection with Refined Contrastive Learning
Zeyu Shangguan, Lian Huai, Tong Liu +1
Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts w…
Distinctive Self-Similar Object Detection
Zeyu Shangguan, Bocheng Hu, Guohua Dai +3
Deep learning-based object detection has demonstrated a significant presence in the practical applications of artificial intelligence. However, objects such as fire and smoke, pose…
edge-SR: Super-Resolution For The Masses
Pablo Navarrete Michelini, Yunhua Lu, Xingqun Jiang
Classic image scaling (e.g. bicubic) can be seen as one convolutional layer and a single upscaling filter. Its implementation is ubiquitous in all display devices and image process…
Back-Projection Pipeline
Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu +1
We propose a simple extension of residual networks that works simultaneously in multiple resolutions. Our network design is inspired by the iterative back-projection algorithm but…
Multi-Grid Back-Projection Networks
Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu +2
Multi-Grid Back-Projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of mul…
A Tour of Convolutional Networks Guided by Linear Interpreters
Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu +1
Convolutional networks are large linear systems divided into layers and connected by non-linear units. These units are the "articulations" that allow the network to adapt to the in…