no code implementations • Findings (EMNLP) 2021 • Xin Lu, Yijian Tian, Yanyan Zhao, Bing Qin
To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework.
no code implementations • 21 Dec 2022 • Lewis Marsh, Felix Y. Zhou, Xiao Qin, Xin Lu, Helen M. Byrne, Heather A. Harrington
Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e. g., brain, liver) in their three-dimensional composition.
no code implementations • 4 Nov 2022 • Jiehua Zhang, Xueyang Zhang, Zhuo Su, Zitong Yu, Yanghe Feng, Xin Lu, Matti Pietikäinen, Li Liu
For ViTs, DyBinaryCCT presents the superiority of the convolutional embedding layer in fully binarized ViTs and achieves 56. 1% on the ImageNet dataset, which is nearly 9% higher than the baseline.
1 code implementation • 11 Oct 2022 • Bo Li, Yongqiang Yao, Jingru Tan, Xin Lu, Fengwei Yu, Ye Luo, Jianwei Lu
Specifically, there are an object detection task (consisting of an instance-classification task and a localization task) and an image-classification task in our framework, responsible for utilizing the two types of supervision.
1 code implementation • 11 Oct 2022 • Jingru Tan, Bo Li, Xin Lu, Yongqiang Yao, Fengwei Yu, Tong He, Wanli Ouyang
Long-tail distribution is widely spread in real-world applications.
no code implementations • 8 Oct 2022 • Weixiang Zhao, Yanyan Zhao, Xin Lu, Bing Qin
As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests.
no code implementations • 7 Oct 2022 • Lei Cui, Yangguang Li, Xin Lu, Dong An, Fenggang Liu
Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model.
1 code implementation • 15 Jun 2022 • Renee S. Hoekzema, Lewis Marsh, Otto Sumray, Thomas M. Carroll, Xin Lu, Helen M. Byrne, Heather A. Harrington
Analysis of single-cell transcriptomics often relies on clustering cells and then performing differential gene expression (DGE) to identify genes that vary between these clusters.
1 code implementation • 8 Jun 2022 • Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, Xinyi Le
For example, when learning a unified model for 15 categories in MVTec-AD, we surpass the second competitor on the tasks of both anomaly detection (from 88. 1% to 96. 5%) and anomaly localization (from 89. 5% to 96. 8%).
1 code implementation • 22 Jan 2022 • Zhiyuan You, Kai Yang, Wenhan Luo, Xin Lu, Lei Cui, Xinyi Le
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i. e., described by one or several support images) occurring in the query image.
Ranked #2 on
Object Counting
on CARPK
1 code implementation • 19 Dec 2021 • Wenbo Li, Xin Lu, Shengju Qian, Jiangbo Lu, Xiangyu Zhang, Jiaya Jia
Pre-training has marked numerous state of the arts in high-level computer vision, while few attempts have ever been made to investigate how pre-training acts in image processing systems.
2 code implementations • 8 Dec 2021 • Xiaojie Chu, Liangyu Chen, Chengpeng Chen, Xin Lu
Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
Ranked #1 on
Image Defocus Deblurring
on DPD
no code implementations • 8 Oct 2021 • Jiehua Zhang, Zhuo Su, Yanghe Feng, Xin Lu, Matti Pietikäinen, Li Liu
The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs.
no code implementations • 17 May 2021 • Andrey Ignatov, Kim Byeoung-su, Radu Timofte, Angeline Pouget, Fenglong Song, Cheng Li, Shuai Xiao, Zhongqian Fu, Matteo Maggioni, Yibin Huang, Shen Cheng, Xin Lu, Yifeng Zhou, Liangyu Chen, Donghao Liu, Xiangyu Zhang, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Minsu Kwon, Myungje Lee, Jaeyoon Yoo, Changbeom Kang, Shinjo Wang, Bin Huang, Tianbao Zhou, Shuai Liu, Lei Lei, Chaoyu Feng, Liguang Huang, Zhikun Lei, Feifei Chen
A detailed description of all models developed in the challenge is provided in this paper.
2 code implementations • 13 May 2021 • Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, Chengpeng Chen
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
Ranked #3 on
Single Image Deraining
on Test2800
1 code implementation • CVPR 2021 • Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu
In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner.
1 code implementation • IWCS (ACL) 2021 • Gene Louis Kim, Viet Duong, Xin Lu, Lenhart Schubert
"Episodic Logic:Unscoped Logical Form" (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism.
no code implementations • 22 Feb 2021 • Feng Du, Shuaishuai Luo, Brenden R. Ortiz, Ye Chen, Weiyin Duan, Dongting Zhang, Xin Lu, Stephen D. Wilson, Yu Song, Huiqiu Yuan
Beyond $p\approx10$ GPa, a second superconducting dome emerges with maximum $T_{\rm c}\approx1. 0$ K at $p_{\rm c2}\approx22$ GPa, which becomes fully suppressed at $p\approx28$ GPa.
Superconductivity
no code implementations • 21 Jan 2021 • Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
2 code implementations • CVPR 2021 • Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, Quanquan Li
To address the problem of imbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that re-balances the training process for each category independently and equally.
Ranked #9 on
Instance Segmentation
on LVIS v1.0 val
no code implementations • COLING 2020 • Xin Lu, Yanyan Zhao, Yang Wu, Yijian Tian, Huipeng Chen, Bing Qin
We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time.
Ranked #28 on
Emotion Recognition in Conversation
on IEMOCAP
no code implementations • ECCV 2020 • Xin Lu, Quanquan Li, Buyu Li, Junjie Yan
In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors.
no code implementations • 28 Jul 2020 • Xin Lu, Mark-Oliver Goerbig
We investigate possible hybridization between these interface states as a function of the width of the topological material and of the characteristic interface size.
Mesoscale and Nanoscale Physics High Energy Physics - Theory Quantum Physics
no code implementations • 7 Jun 2020 • Xin Lu
In machine learning, observation features are measured in a metric space to obtain their distance function for optimization.
145 code implementations • 17 Jun 2019 • Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin
In this paper, we introduce the various features of this toolbox.
2 code implementations • 13 Jun 2019 • Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
Grid R-CNN is a well-performed objection detection framework.
no code implementations • 15 Apr 2019 • Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden, James East, Xin Lu, Jens Rittscher
Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.
no code implementations • CVPR 2019 • Wei Xiong, Jiahui Yu, Zhe Lin, Jimei Yang, Xin Lu, Connelly Barnes, Jiebo Luo
We show that by such disentanglement, the contour completion model predicts reasonable contours of objects, and further substantially improves the performance of image inpainting.
2 code implementations • CVPR 2019 • Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.
Ranked #138 on
Object Detection
on COCO minival
no code implementations • ECCV 2018 • Rameswar Panda, Jianming Zhang, Haoxiang Li, Joon-Young Lee, Xin Lu, Amit K. Roy-Chowdhury
While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts.
1 code implementation • ECCV 2018 • Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame.
30 code implementations • ICCV 2019 • Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas Huang
We present a generative image inpainting system to complete images with free-form mask and guidance.
Ranked #3 on
Image Inpainting
on Places2 val
2 code implementations • ICLR 2018 • Jianbo Ye, Xin Lu, Zhe Lin, James Z. Wang
Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios.
1 code implementation • CVPR 2018 • Licheng Yu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, Tamara L. Berg
In this paper, we address referring expression comprehension: localizing an image region described by a natural language expression.
Ranked #12 on
Referring Expression Segmentation
on RefCOCO+ testA
28 code implementations • CVPR 2018 • Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas S. Huang
Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
1 code implementation • ICCV 2017 • Wei-Chih Hung, Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, Ming-Hsuan Yang
We present a scene parsing method that utilizes global context information based on both the parametric and non- parametric models.
14 code implementations • NeurIPS 2017 • Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
1 code implementation • ICCV 2017 • Chenxi Liu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Alan Yuille
In this paper we are interested in the problem of image segmentation given natural language descriptions, i. e. referring expressions.
no code implementations • CVPR 2017 • Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.
2 code implementations • CVPR 2017 • Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, Ming-Hsuan Yang
Compositing is one of the most common operations in photo editing.
1 code implementation • CVPR 2017 • Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, Hao Li
Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal.
no code implementations • ICCV 2015 • Xin Lu, Zhe Lin, Xiaohui Shen, Radomir Mech, James Z. Wang
We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image.
Ranked #8 on
Aesthetics Quality Assessment
on AVA