no code implementations • 5 Feb 2024 • Yuqian Fu, Yu Wang, Yixuan Pan, Lian Huai, Xingyu Qiu, Zeyu Shangguan, Tong Liu, Yanwei Fu, Luc van Gool, Xingqun Jiang
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.
no code implementations • 20 Nov 2023 • Zeyu Shangguan, Lian Huai, Tong Liu, Xingqun Jiang
We also explore various types of skip connection between the encoder and decoder for DETR.
no code implementations • 24 Nov 2022 • Zeyu Shangguan, Lian Huai, Tong Liu, Xingqun Jiang
A pre-determination component is introduced to find out the Resemblance Group from novel classes which contains confusable classes.
no code implementations • 20 Nov 2022 • Zeyu Shangguan, Bocheng Hu, Guohua Dai, Yuyu Liu, Darun Tang, Xingqun Jiang
However, objects such as fire and smoke, pose challenges to object detection because of their non-solid and various shapes, and consequently difficult to truly meet requirements in practical fire prevention and control.
1 code implementation • 23 Aug 2021 • Pablo Navarrete Michelini, Yunhua Lu, Xingqun Jiang
We explore possible solutions to this problem with the aim to fill the gap between classic upscalers and small deep learning configurations.
no code implementations • 25 Jan 2021 • Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, Xingqun Jiang
We propose a simple extension of residual networks that works simultaneously in multiple resolutions.
no code implementations • 1 Jan 2021 • Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu, Xingqun Jiang
For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output.
1 code implementation • ICCV 2019 • Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, Xingqun Jiang
These units are the "articulations" that allow the network to adapt to the input.