no code implementations • NAACL (SUKI) 2022 • Xiaofeng Chen, YiRong Chen, Xiaofen Xing, Xiangmin Xu, Wenjing Han, Qianfeng Tie
Because of the compositionality of natural language, syntactic structure which contains the information about the relationship between words is a key factor for semantic understanding.
no code implementations • 6 Jul 2022 • Kan Wang, Changxing Ding, Jianxin Pang, Xiangmin Xu
In this work, we propose a novel Context Sensing Attention Network (CSA-Net), which improves both the frame feature extraction and temporal aggregation steps.
1 code implementation • 29 May 2022 • YiRong Chen, Weiquan Fan, Xiaofen Xing, Jianxin Pang, Minlie Huang, Wenjing Han, Qianfeng Tie, Xiangmin Xu
Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.
Ranked #1 on
Emotion Recognition in Conversation
on CPED
no code implementations • 12 Apr 2022 • Kailing Guo, Zhenquan Lin, Xiaofen Xing, Fang Liu, Xiangmin Xu
In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
1 code implementation • 9 Oct 2021 • Zhenquan Lin, Kailing Guo, Xiaofen Xing, Xiangmin Xu
Comprehensive experiments show that WE outperforms the other reactivation methods and plug-in training methods with typical convolutional neural networks, especially lightweight networks.
no code implementations • 3 Mar 2021 • Tong Shen, Gyorgy Lur, Xiangmin Xu, Zhaoxia Yu
With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions.
no code implementations • 30 Jan 2021 • Weiquan Fan, Xiangmin Xu, Xiaofen Xing, Weidong Chen, DongYan Huang
Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI).
1 code implementation • 14 May 2019 • Weirui Lu, Xiaofen Xing, Bolun Cai, Xiangmin Xu
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
no code implementations • 23 Apr 2019 • Bolun Cai, Xiangmin Xu, Xiaofen Xing, Kui Jia, Jie Miao, DaCheng Tao
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion.
no code implementations • 27 Dec 2017 • Nitin Agarwal, Xiangmin Xu, Gopi Meenakshisundaram
In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space.
no code implementations • 4 Dec 2017 • Bolun Cai, Xiangmin Xu, Kailing Guo, Kui Jia, DaCheng Tao
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
2 code implementations • 25 Jun 2017 • Suo Qiu, Xiangmin Xu, Bolun Cai
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks.
no code implementations • 9 Jun 2017 • Jie Miao, Xiangmin Xu, Xiaofen Xing, DaCheng Tao
However, complex temporal variations require high-level semantic representations to fully achieve temporal slowness, and thus it is impractical to learn a high-level representation from dynamic textures directly by SFA.
no code implementations • 15 May 2017 • Xiaoyi Jia, Xiangmin Xu, Bolun Cai, Kailing Guo
However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output.
1 code implementation • 8 Feb 2017 • Lingke Zeng, Xiangmin Xu, Bolun Cai, Suo Qiu, Tong Zhang
Crowd counting on static images is a challenging problem due to scale variations.
3 code implementations • 28 Jan 2016 • Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, DaCheng Tao
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Ranked #5 on
Image Dehazing
on KITTI