2 code implementations • 31 Dec 2023 • Dimitrios Psychogyios, Emanuele Colleoni, Beatrice van Amsterdam, Chih-Yang Li, Shu-Yu Huang, Yuchong Li, Fucang Jia, Baosheng Zou, Guotai Wang, Yang Liu, Maxence Boels, Jiayu Huo, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin, Mengya Xu, An Wang, Yanan Wu, Long Bai, Hongliang Ren, Atsushi Yamada, Yuriko Harai, Yuto Ishikawa, Kazuyuki Hayashi, Jente Simoens, Pieter DeBacker, Francesco Cisternino, Gabriele Furnari, Alex Mottrie, Federica Ferraguti, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Soohee Kim, Seung Hyun Lee, Kyu Eun Lee, Hyoun-Joong Kong, Kui Fu, Chao Li, Shan An, Stefanie Krell, Sebastian Bodenstedt, Nicolas Ayobi, Alejandra Perez, Santiago Rodriguez, Juanita Puentes, Pablo Arbelaez, Omid Mohareri, Danail Stoyanov
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
no code implementations • CVPR 2021 • Luwei Hou, Yu Zhang, Kui Fu, Jia Li
Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i. e. with object bounding boxes) to train object detectors for target domains that are weakly labeled (i. e. with image-level tags).
Ranked #4 on Weakly Supervised Object Detection on Clipart1k
no code implementations • 2 Sep 2020 • Kui Fu, Jia Li, Lin Ma, Kai Mu, Yonghong Tian
In this paper, we propose a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects.
no code implementations • 10 Apr 2019 • Jia Li, Kui Fu, Shengwei Zhao, Shiming Ge
In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model.
no code implementations • 9 Apr 2019 • Kui Fu, Peipei Shi, Yafei Song, Shiming Ge, Xiangju Lu, Jia Li
To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net.
no code implementations • 25 Nov 2018 • Jia Li, Daowei Li, Kui Fu, Long Xu
Visual attention prediction is a classic problem that seems to be well addressed in the deep learning era.
no code implementations • 14 Nov 2018 • Kui Fu, Jia Li, Yu Zhang, Hongze Shen, Yonghong Tian
After that, the visual saliency knowledge encoded in the most representative paths is selected and aggregated to improve the capability of MM-Net in predicting spatial saliency in aerial scenarios.