Search Results for author: Guangming Lu

Found 42 papers, 26 papers with code

Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction

no code implementations Findings (EMNLP) 2021 Guimin Hu, Guangming Lu, Yi Zhao

Moreover, we quantify the effect of context on emotion cause extraction and provide the visualization of the interactions between candidate cause clauses and contexts.

Emotion Cause Extraction Graph Attention

BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models

1 code implementation23 Jan 2024 Feng Lin, Hanling Yi, Hongbin Li, Yifan Yang, Xiaotian Yu, Guangming Lu, Rong Xiao

Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency.

TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit

no code implementations15 Jan 2024 Yihan Cao, Xu Chen, Lun Du, Hao Chen, Qiang Fu, Shi Han, Yushu Du, Yanbin Kang, Guangming Lu, Zi Li

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation.

Saliency-Aware Regularized Graph Neural Network

no code implementations1 Jan 2024 Wenjie Pei, Weina Xu, Zongze Wu, Weichao Li, Jinfan Wang, Guangming Lu, Xiangrong Wang

In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone.

Graph Classification Representation Learning +2

Professional Network Matters: Connections Empower Person-Job Fit

no code implementations19 Dec 2023 Hao Chen, Lun Du, Yuxuan Lu, Qiang Fu, Xu Chen, Shi Han, Yanbin Kang, Guangming Lu, Zi Li

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions.

Robust 3D Tracking with Quality-Aware Shape Completion

no code implementations17 Dec 2023 Jingwen Zhang, Zikun Zhou, Guangming Lu, Jiandong Tian, Wenjie Pei

Considering that, we propose to construct a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking.

3D Single Object Tracking Object Tracking

SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt

1 code implementation16 Dec 2023 Wenjie Pei, Tongqi Xia, Fanglin Chen, Jinsong Li, Jiandong Tian, Guangming Lu

Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming from NLP, which represents an input image as a flattened sequence of token embeddings and then learns a set of unordered parameterized tokens prefixed to the sequence representation as the visual prompts for task adaptation of large vision models.

Image Classification

D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition

1 code implementation3 Dec 2023 Wenjie Pei, Qizhong Tan, Guangming Lu, Jiandong Tian

Adapting large pre-trained image models to few-shot action recognition has proven to be an effective and efficient strategy for learning robust feature extractors, which is essential for few-shot learning.

Few-Shot action recognition Few Shot Action Recognition +1

Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining

1 code implementation13 Sep 2023 Xianghao Zhan, Qinmei Xu, Yuanning Zheng, Guangming Lu, Olivier Gevaert

This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data.

Conformal Prediction

Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption Detection

1 code implementation ICCV 2023 Xin Feng, Yifeng Xu, Guangming Lu, Wenjie Pei

Detecting corrupted regions by learning the contrastive distinctions rather than the semantic patterns of corruptions, our model has well generalization ability across different corruption patterns.

Contrastive Learning Image Inpainting +1

Scene-Generalizable Interactive Segmentation of Radiance Fields

no code implementations9 Aug 2023 Songlin Tang, Wenjie Pei, Xin Tao, Tanghui Jia, Guangming Lu, Yu-Wing Tai

Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their applicability.

Interactive Segmentation Segmentation +1

Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement

1 code implementation6 Aug 2023 Zhenhua Ning, Zhuotao Tian, Guangming Lu, Wenjie Pei

Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge.

Point Cloud Segmentation Segmentation

Joint adjustment image steganography networks

1 code implementation Signal Processing: Image Communication 2023 Le Zhang, Yao Lu, Tong Li, Guangming Lu

Thus, the security and quality of stego and revealed secret images still have much room for promotion, especially for large-capacity image steganography.

Image Steganography Steganographics

Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction

1 code implementation24 Feb 2023 Guimin Hu, Yi Zhao, Guangming Lu

Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text.

Emotion Cause Extraction Emotion-Cause Pair Extraction

Universal Object Detection with Large Vision Model

1 code implementation19 Dec 2022 Feng Lin, Wenze Hu, YaoWei Wang, Yonghong Tian, Guangming Lu, Fanglin Chen, Yong Xu, Xiaoyu Wang

In this study, our focus is on a specific challenge: the large-scale, multi-domain universal object detection problem, which contributes to the broader goal of achieving a universal vision system.

Object object-detection +1

Activating the Discriminability of Novel Classes for Few-shot Segmentation

no code implementations2 Dec 2022 Dianwen Mei, Wei Zhuo, Jiandong Tian, Guangming Lu, Wenjie Pei

To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation.


Semantic-Aware Local-Global Vision Transformer

no code implementations27 Nov 2022 Jiatong Zhang, Zengwei Yao, Fanglin Chen, Guangming Lu, Wenjie Pei

Second, instead of only performing local self-attention within local windows as Swin Transformer does, the proposed SALG performs both 1) local intra-region self-attention for learning fine-grained features within each region and 2) global inter-region feature propagation for modeling global dependencies among all regions.

Image Classification Semantic Segmentation

UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

1 code implementation21 Nov 2022 Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, Yongbin Li

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors.

Contrastive Learning Emotion Recognition in Conversation +1

DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms

no code implementations22 Oct 2022 Feifan Li, Lun Du, Qiang Fu, Shi Han, Yushu Du, Guangming Lu, Zi Li

Furthermore, based on the dynamic user intent representations, we propose a meta predictor to perform differentiated user engagement forecasting.

Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification

1 code implementation CVPR 2022 Xinyu Lin, Jinxing Li, Zeyu Ma, Huafeng Li, Shuang Li, Kaixiong Xu, Guangming Lu, David Zhang

Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID.

Cross-Modal Retrieval Person Re-Identification +2

Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations

no code implementations25 Jul 2022 Wenjie Pei, Shuang Wu, Dianwen Mei, Fanglin Chen, Jiandong Tian, Guangming Lu

In this work we design a novel knowledge distillation framework to guide the learning of the object detector and thereby restrain the overfitting in both the pre-training stage on base classes and fine-tuning stage on novel classes.

Few-Shot Object Detection Knowledge Distillation +2

Learning Generalizable Latent Representations for Novel Degradations in Super Resolution

no code implementations25 Jul 2022 Fengjun Li, Xin Feng, Fanglin Chen, Guangming Lu, Wenjie Pei

The real-world degradations can be beyond the simulation scope by the handcrafted degradations, which are referred to as novel degradations.

Blind Super-Resolution Image Super-Resolution +1

Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection

1 code implementation22 Jul 2022 Shuang Wu, Wenjie Pei, Dianwen Mei, Fanglin Chen, Jiandong Tian, Guangming Lu

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy.

Few-Shot Object Detection object-detection

Learning Sequence Representations by Non-local Recurrent Neural Memory

1 code implementation20 Jul 2022 Wenjie Pei, Xin Feng, Canmiao Fu, Qiong Cao, Guangming Lu, Yu-Wing Tai

The key challenge of sequence representation learning is to capture the long-range temporal dependencies.

Representation Learning

Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration

1 code implementation16 Jul 2022 Xin Feng, Haobo Ji, Wenjie Pei, Fanglin Chen, Guangming Lu

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e. g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data.

Image Dehazing Image Restoration +2

Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition

1 code implementation15 Jul 2022 Jingjing Wu, Pengyuan Lyu, Guangming Lu, Chengquan Zhang, Wenjie Pei

Typical text spotters follow the two-stage spotting paradigm which detects the boundary for a text instance first and then performs text recognition within the detected regions.

Text Detection Text Spotting

Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling

no code implementations4 Jul 2022 Canran Li, Dongnan Liu, Haoran Li, Zheng Zhang, Guangming Lu, Xiaojun Chang, Weidong Cai

In this work, we propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification.

Classification Instance Segmentation +3

FaceMap: Towards Unsupervised Face Clustering via Map Equation

1 code implementation21 Mar 2022 Xiaotian Yu, Yifan Yang, Aibo Wang, Ling Xing, Hanling Yi, Guangming Lu, Xiaoyu Wang

Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management.

Clustering Community Detection +3

U2-Former: A Nested U-shaped Transformer for Image Restoration

no code implementations4 Dec 2021 Haobo Ji, Xin Feng, Wenjie Pei, Jinxing Li, Guangming Lu

While Transformer has achieved remarkable performance in various high-level vision tasks, it is still challenging to exploit the full potential of Transformer in image restoration.

Computational Efficiency Contrastive Learning +3

Pedestrian Detection by Exemplar-Guided Contrastive Learning

no code implementations17 Nov 2021 Zebin Lin, Wenjie Pei, Fanglin Chen, David Zhang, Guangming Lu

Instead of learning each of these diverse pedestrian appearance features individually as most existing methods do, we propose to perform contrastive learning to guide the feature learning in such a way that the semantic distance between pedestrians with different appearances in the learned feature space is minimized to eliminate the appearance diversities, whilst the distance between pedestrians and background is maximized.

Contrastive Learning Pedestrian Detection

Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain

no code implementations10 Oct 2021 Zengwei Yao, Wenjie Pei, Fanglin Chen, Guangming Lu, David Zhang

Existing methods for speech separation either transform the speech signals into frequency domain to perform separation or seek to learn a separable embedding space by constructing a latent domain based on convolutional filters.

speech-recognition Speech Recognition +1

Generative Memory-Guided Semantic Reasoning Model for Image Inpainting

no code implementations1 Oct 2021 Xin Feng, Wenjie Pei, Fengjun Li, Fanglin Chen, David Zhang, Guangming Lu

Most existing methods for image inpainting focus on learning the intra-image priors from the known regions of the current input image to infer the content of the corrupted regions in the same image.

Image Inpainting

Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification

1 code implementation26 Jun 2021 Huafeng Li, Kaixiong Xu, Jinxing Li, Guangming Lu, Yong Xu, Zhengtao Yu, David Zhang

Since human-labeled samples are free for the target set, unsupervised person re-identification (Re-ID) has attracted much attention in recent years, by additionally exploiting the source set.

Disentanglement Domain Adaptation +2

DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation

1 code implementation12 Jun 2021 Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, Guangming Lu

To alleviate these problems, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first attempt to concurrently incorporate the advantages of hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture to enhance the semantic segmentation quality of varying medical images.

Image Segmentation Medical Image Segmentation +2

Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing

1 code implementation CVPR 2021 Xunguang Wang, Zheng Zhang, Baoyuan Wu, Fumin Shen, Guangming Lu

However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field.

Deep Hashing Image Retrieval +1

Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images

1 code implementation9 Oct 2020 Xin Feng, Wenjie Pei, Zihui Jia, Fanglin Chen, David Zhang, Guangming Lu

In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.

Image Dehazing Image Generation +3

Pruning Filter in Filter

1 code implementation NeurIPS 2020 Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun

Through extensive experiments, we demonstrate that SWP is more effective compared to the previous FP-based methods and achieves the state-of-art pruning ratio on CIFAR-10 and ImageNet datasets without obvious accuracy drop.

Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation

1 code implementation26 Apr 2020 Hao Cheng, Fanxu Meng, Ke Li, Yuting Gao, Guangming Lu, Xing Sun, Rongrong Ji

To gain a universal improvement on both valid and invalid filters, we compensate grafting with distillation (\textbf{Cultivation}) to overcome the drawback of grafting .


Dual Asymmetric Deep Hashing Learning

1 code implementation25 Jan 2018 Jinxing Li, Bob Zhang, Guangming Lu, David Zhang

The deep hash functions are then learned through two networks by minimizing the gap between the learned features and discrete codes.

Deep Hashing

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