no code implementations • 11 Mar 2024 • Hongguang Pan, Zhuoyi Li, Yunpeng Fu, Xuebin Qin, Jianchen Hu
Considering the advantages of low cost and easy portability of the electroencephalogram (EEG) acquisition equipments, we propose a novel image reconstruction method based on EEG signals in this paper.
1 code implementation • 24 May 2023 • JiaYi Zhu, Xuebin Qin, Abdulmotaleb Elsaddik
In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map.
1 code implementation • 22 Mar 2022 • Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Ying Tai, Chengjie Wang, Ling Shao
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.
1 code implementation • 6 Mar 2022 • Xuebin Qin, Hang Dai, Xiaobin Hu, Deng-Ping Fan, Ling Shao, and Luc Van Gool
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images.
Ranked #5 on Dichotomous Image Segmentation on DIS-TE1
3 code implementations • 31 Dec 2021 • Deng-Ping Fan, Ziling Huang, Peng Zheng, Hong Liu, Xuebin Qin, Luc van Gool
Besides, we elaborate comprehensive experiments on the existing 19 cutting-edge models.
no code implementations • NAACL 2021 • Chenyang Huang, Amine Trabelsi, Xuebin Qin, Nawshad Farruque, Lili Mou, Osmar Za{\"\i}ane
Multi-label emotion classification is an important task in NLP and is essential to many applications.
no code implementations • 11 Feb 2021 • Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
5 code implementations • 12 Jan 2021 • Xuebin Qin, Deng-Ping Fan, Chenyang Huang, Cyril Diagne, Zichen Zhang, Adrià Cabeza Sant'Anna, Albert Suàrez, Martin Jagersand, Ling Shao
In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.
28 code implementations • 18 May 2020 • Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zaiane, Martin Jagersand
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on Salient Object Detection on SOD
no code implementations • 6 Nov 2019 • Chenyang Huang, Amine Trabelsi, Xuebin Qin, Nawshad Farruque, Osmar R. Zaïane
Most of the existing methods treat this task as a problem of single-label multi-class text classification.
3 code implementations • CVPR 2019 • Xuebin Qin, Zichen Zhang, Chenyang Huang, Chao Gao, Masood Dehghan, Martin Jagersand
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #3 on RGB Salient Object Detection on SOC
1 code implementation • 10 Aug 2017 • Shida He, Xuebin Qin, Zichen Zhang, Martin Jagersand
This approach reduces a 3D line segment fitting problem into two 2D line segment fitting problems and takes advantage of both images and depth maps.
2 code implementations • 30 Apr 2017 • Xuebin Qin, Shida He, Camilo Perez Quintero, Abhineet Singh, Masood Dehghan, Martin Jagersand
The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one.