Search Results for author: Fu Lee Wang

Found 18 papers, 5 papers with code

A Self-supervised Joint Training Framework for Document Reranking

no code implementations Findings (NAACL) 2022 Xiaozhi Zhu, Tianyong Hao, Sijie Cheng, Fu Lee Wang, Hai Liu

Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks.

Language Modelling Passage Ranking

Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval

no code implementations24 Mar 2023 Mingqiang Wei, Qian Sun, Haoran Xie, Dong Liang, Fu Lee Wang

Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.


RainDiffusion: When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining

no code implementations23 Jan 2023 Mingqiang Wei, Yiyang Shen, Yongzhen Wang, Haoran Xie, Jing Qin, Fu Lee Wang

Before answering it, we observe two major obstacles of diffusion models in real-world image deraining: the need for paired training data and the limited utilization of multi-scale rain patterns.

Rain Removal Translation

GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising

no code implementations28 Oct 2022 Zhaowei Chen, Peng Li, Zeyong Wei, Honghua Chen, Haoran Xie, Mingqiang Wei, Fu Lee Wang

We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).


SPCNet: Stepwise Point Cloud Completion Network

no code implementations5 Sep 2022 Fei Hu, Honghua Chen, Xuequan Lu, Zhe Zhu, Jun Wang, Weiming Wang, Fu Lee Wang, Mingqiang Wei

We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.

Point Cloud Completion

TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

1 code implementation3 Sep 2022 Yongzhen Wang, Xuefeng Yan, Kaiwen Zhang, Lina Gong, Haoran Xie, Fu Lee Wang, Mingqiang Wei

Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios.

Image Dehazing Image Restoration +3

Contrastive Semantic-Guided Image Smoothing Network

1 code implementation2 Sep 2022 Jie Wang, Yongzhen Wang, Yidan Feng, Lina Gong, Xuefeng Yan, Haoran Xie, Fu Lee Wang, Mingqiang Wei

Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.

image smoothing Semantic Segmentation

PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection

no code implementations29 Aug 2022 Peng Wu, Lipeng Gu, Xuefeng Yan, Haoran Xie, Fu Lee Wang, Gary Cheng, Mingqiang Wei

Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.

3D Object Detection object-detection +1

GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling

1 code implementation14 Jul 2022 Chen Chen, Yisen Wang, Honghua Chen, Xuefeng Yan, Dayong Ren, Yanwen Guo, Haoran Xie, Fu Lee Wang, Mingqiang Wei

Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.

Semantic Segmentation

UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning

1 code implementation4 May 2022 Yongzhen Wang, Xuefeng Yan, Fu Lee Wang, Haoran Xie, Wenhan Yang, Mingqiang Wei, Jing Qin

From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.

Contrastive Learning Image Dehazing

Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal

no code implementations28 Apr 2022 Yiyang Shen, Yongzhen Wang, Mingqiang Wei, Honghua Chen, Haoran Xie, Gary Cheng, Fu Lee Wang

Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.

Depth Estimation Depth Prediction +1

When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters

1 code implementation1 Mar 2022 Fu Lee Wang, Yidan Feng, Haoran Xie, Gary Cheng, Mingqiang Wei

Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks.

Denoising Rain Removal

Topic Driven Adaptive Network for Cross-Domain Sentiment Classification

no code implementations28 Nov 2021 Yicheng Zhu, Yiqiao Qiu, Qingyuan Wu, Fu Lee Wang, Yanghui Rao

In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space.

Classification Domain Adaptation +3

Neural Mixed Counting Models for Dispersed Topic Discovery

no code implementations ACL 2020 Jiemin Wu, Yanghui Rao, Zusheng Zhang, Haoran Xie, Qing Li, Fu Lee Wang, Ziye Chen

Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.

Variational Inference

Siamese Network-Based Supervised Topic Modeling

no code implementations EMNLP 2018 Minghui Huang, Yanghui Rao, Yuwei Liu, Haoran Xie, Fu Lee Wang

Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification.

General Classification Sentiment Analysis +3

A Network Framework for Noisy Label Aggregation in Social Media

no code implementations ACL 2017 Xueying Zhan, Yao-Wei Wang, Yanghui Rao, Haoran Xie, Qing Li, Fu Lee Wang, Tak-Lam Wong

This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.

Cultural Vocal Bursts Intensity Prediction Image Classification +2

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