Search Results for author: Jun-Jie Huang

Found 18 papers, 6 papers with code

SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks

1 code implementation4 Jun 2024 Yi Pan, Jun-Jie Huang, Zihan Chen, Wentao Zhao, Ziyue Wang

Robust and imperceptible adversarial video attack is challenging due to the spatial and temporal characteristics of videos.

Adversarial Attack

Learning-Based Reconstruction of FRI Signals

1 code implementation16 Dec 2022 Vincent C. H. Leung, Jun-Jie Huang, Yonina C. Eldar, Pier Luigi Dragotti

While the deep unfolded network achieves similar performance as the classical FRI techniques and outperforms the encoder-decoder network in the low noise regimes, the latter allows to reconstruct the FRI signal even when the sampling kernel is unknown.

Decoder Denoising

A Fast Automatic Method for Deconvoluting Macro X-ray Fluorescence Data Collected from Easel Paintings

no code implementations31 Oct 2022 Su Yan, Jun-Jie Huang, Herman Verinaz-Jadan, Nathan Daly, Catherine Higgitt, Pier Luigi Dragotti

Macro X-ray Fluorescence (MA-XRF) scanning is increasingly widely used by researchers in heritage science to analyse easel paintings as one of a suite of non-invasive imaging techniques.

FAD

DURRNet: Deep Unfolded Single Image Reflection Removal Network

no code implementations12 Mar 2022 Jun-Jie Huang, Tianrui Liu, Zhixiong Yang, Shaojing Fu, Wentao Zhao, Pier Luigi Dragotti

With the deep unrolling technique, we build the DURRNet with ProxNets to model natural image priors and ProxInvNets which are constructed with invertible networks to impose the exclusion prior.

blind source separation Reflection Removal +1

Mixed X-Ray Image Separation for Artworks with Concealed Designs

no code implementations23 Jan 2022 Wei Pu, Jun-Jie Huang, Barak Sober, Nathan Daly, Catherine Higgitt, Ingrid Daubechies, Pier Luigi Dragotti, Miguel Rodigues

In this paper, we focus on X-ray images of paintings with concealed sub-surface designs (e. g., deriving from reuse of the painting support or revision of a composition by the artist), which include contributions from both the surface painting and the concealed features.

Rolling Shutter Correction

WINNet: Wavelet-inspired Invertible Network for Image Denoising

1 code implementation14 Sep 2021 Jun-Jie Huang, Pier Luigi Dragotti

The proposed WINNet consists of K-scale of lifting inspired invertible neural networks (LINNs) and sparsity-driven denoising networks together with a noise estimation network.

Deblurring Image Deblurring +2

Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net

no code implementations19 Jun 2021 Tianrui Liu, Qingjie Meng, Jun-Jie Huang, Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz

Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.

reinforcement-learning Reinforcement Learning (RL) +1

LINN: Lifting Inspired Invertible Neural Network for Image Denoising

1 code implementation7 May 2021 Jun-Jie Huang, Pier Luigi Dragotti

In this paper, we propose an invertible neural network for image denoising (DnINN) inspired by the transform-based denoising framework.

Image Denoising

Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization

1 code implementation9 Sep 2020 Jingyuan Xia, Shengxi Li, Jun-Jie Huang, Imad Jaimoukha, Deniz Gunduz

In this paper, we propose a novel solution for non-convex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of sub-problems corresponding to each variable, and then iteratively optimize each sub-problem using a fixed updating rule.

Matrix Completion Meta-Learning

Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking

no code implementations27 Aug 2020 Zhibo Zou, Jun-Jie Huang, Ping Luo

Based on simple and traditional methods, we propose a compensation tracker to further alleviate the lost tracking problem caused by missing detection.

Motion Compensation Multi-Object Tracking +1

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

no code implementations27 Jul 2020 Bingbing Xu, Jun-Jie Huang, Liang Hou, Hua-Wei Shen, Jinhua Gao, Xue-Qi Cheng

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node.

Classification General Classification +3

Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries

no code implementations31 Jan 2020 Jun-Jie Huang, Pier Luigi Dragotti

By exploiting the properties of a convolutional dictionary, we present an efficient convolutional analysis dictionary learning approach.

Clustering Dictionary Learning +1

Learning Deep Analysis Dictionaries for Image Super-Resolution

no code implementations31 Jan 2020 Jun-Jie Huang, Pier Luigi Dragotti

Inspired by the recent success of deep neural networks and the recent efforts to develop multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) which is optimized to address a specific regression task known as single image super-resolution.

Clustering Image Super-Resolution +1

Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling

no code implementations18 Dec 2019 Tianrui Liu, Wenhan Luo, Lin Ma, Jun-Jie Huang, Tania Stathaki, Tianhong Dai

Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.

Occlusion Handling Pedestrian Detection

Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection

no code implementations25 Oct 2019 Tianrui Liu, Jun-Jie Huang, Tianhong Dai, Guangyu Ren, Tania Stathaki

In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.

Pedestrian Detection

Modeling Semantic Compositionality with Sememe Knowledge

1 code implementation ACL 2019 Fanchao Qi, Jun-Jie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun

In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment.

multi-word expression embedding multi-word expression sememe prediction

Action Machine: Rethinking Action Recognition in Trimmed Videos

no code implementations14 Dec 2018 Jiagang Zhu, Wei Zou, Liang Xu, Yiming Hu, Zheng Zhu, Manyu Chang, Jun-Jie Huang, Guan Huang, Dalong Du

On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97. 2% and 94. 3% on cross-view and cross-subject respectively.

Action Recognition Multimodal Activity Recognition +3

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