Search Results for author: Ming Wu

Found 27 papers, 16 papers with code

Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection

1 code implementation27 Dec 2023 Jinbo Hu, Yin Cao, Ming Wu, Qiuqiang Kong, Feiran Yang, Mark D. Plumbley, Jun Yang

In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments.

Meta-Learning Sound Event Localization and Detection

META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection

no code implementations17 Aug 2023 Jinbo Hu, Yin Cao, Ming Wu, Feiran Yang, Ziying Yu, Wenwu Wang, Mark D. Plumbley, Jun Yang

For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences in the validation and evaluation stages.

Meta-Learning Sound Event Localization and Detection

Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images

1 code implementation8 Jul 2023 Yi Zhong, Mengqiu Xu, Kongming Liang, Kaixin Chen, Ming Wu

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections.

Image Segmentation Medical Image Segmentation +2

Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations

1 code implementation21 Feb 2023 Xun Zhu, Yutong Xiong, Ming Wu, Gaozhen Nie, Bin Zhang, Ziheng Yang

To the best of our knowledge, our Weather2K is the first attempt to tackle weather forecasting task by taking full advantage of the strengths of observation data from ground weather stations.

Spatio-Temporal Forecasting Time Series Forecasting +1

Zero-shot Clarifying Question Generation for Conversational Search

no code implementations30 Jan 2023 Zhenduo Wang, Yuancheng Tu, Corby Rosset, Nick Craswell, Ming Wu, Qingyao Ai

In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation.

Conversational Search Natural Questions +3

SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines

no code implementations19 Jan 2023 Shizun Wang, Weihong Zeng, Xu Wang, Hao Yang, Li Chen, Yi Yuan, Yunzhao Zeng, Min Zheng, Chuang Zhang, Ming Wu

To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works.

Pre-trained Language Models Can be Fully Zero-Shot Learners

2 code implementations14 Dec 2022 Xuandong Zhao, Siqi Ouyang, Zhiguo Yu, Ming Wu, Lei LI

How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data?

Retrieval text-classification +3

Privileged Prior Information Distillation for Image Matting

no code implementations25 Nov 2022 Cheng Lyu, Jiake Xie, Bo Xu, Cheng Lu, Han Huang, Xin Huang, Ming Wu, Chuang Zhang, Yong Tang

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.

Image Matting

Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention

2 code implementations5 Jan 2022 Haotian Yan, Chuang Zhang, Ming Wu

In this paper, we succeed in introducing multi-scale representations into semantic segmentation ViT via window attention mechanism and further improves the performance and efficiency.

Image Classification Segmentation +1

SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

1 code implementation30 Nov 2021 Shizun Wang, Ming Lu, Kaixin Chen, Jiaming Liu, Xiaoqi Li, Chuang Zhang, Ming Wu

However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.

Data Augmentation Image Super-Resolution

ConTNet: Why not use convolution and transformer at the same time?

2 code implementations27 Apr 2021 Haotian Yan, Zhe Li, Weijian Li, Changhu Wang, Ming Wu, Chuang Zhang

It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.

Image Classification object-detection +1

Contextual Graph Reasoning Networks

no code implementations1 Jan 2021 Zhaoqing Wang, Jiaming Liu, Yangyuxuan Kang, Mingming Gong, Chuang Zhang, Ming Lu, Ming Wu

Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.

2D Human Pose Estimation Instance Segmentation +4

Adaptive Self-training for Neural Sequence Labeling with Few Labels

no code implementations1 Jan 2021 Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah

Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.

Meta-Learning named-entity-recognition +3

Adaptive Self-training for Few-shot Neural Sequence Labeling

no code implementations7 Oct 2020 Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah

While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.

Meta-Learning named-entity-recognition +3

GINet: Graph Interaction Network for Scene Parsing

1 code implementation ECCV 2020 Tianyi Wu, Yu Lu, Yu Zhu, Chuang Zhang, Ming Wu, Zhanyu Ma, Guodong Guo

GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.

Scene Parsing

FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images

no code implementations15 Mar 2020 Kaiyan Chen, Ming Wu, Jiaming Liu, Chuang Zhang

To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD.

C-DLinkNet: considering multi-level semantic features for human parsing

1 code implementation31 Jan 2020 Yu Lu, Muyan Feng, Ming Wu, Chuang Zhang

Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human.

Human Parsing Segmentation +1

Learning Feature Interactions with Lorentzian Factorization Machine

2 code implementations22 Nov 2019 Canran Xu, Ming Wu

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions.

Click-Through Rate Prediction

Towards Efficient Large-Scale Graph Neural Network Computing

no code implementations19 Oct 2018 Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai

This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for.

graph partitioning Knowledge Graphs

RPC Considered Harmful: Fast Distributed Deep Learning on RDMA

no code implementations22 May 2018 Jilong Xue, Youshan Miao, Cheng Chen, Ming Wu, Lintao Zhang, Lidong Zhou

Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.

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