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Found 17 papers, 4 papers with code
Learning-based Reconstruction of FRI Signals
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.
A Fast Automatic Method for Deconvoluting Macro X-ray Fluorescence Data Collected from Easel Paintings
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.
DURRNet: Deep Unfolded Single Image Reflection Removal Network
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.
Mixed X-Ray Image Separation for Artworks with Concealed Designs
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.
WINNet: Wavelet-inspired Invertible Network for Image Denoising
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.
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
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.
LINN: Lifting Inspired Invertible Neural Network for Image Denoising
In this paper, we propose an invertible neural network for image denoising (DnINN) inspired by the transform-based denoising framework.
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
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.
Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking
Based on simple and traditional methods, we propose a compensation tracker to further alleviate the lost tracking problem caused by missing detection.
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification
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.
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i. e., the question-answer pairs.
Learning Deep Analysis Dictionaries for Image Super-Resolution
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.
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries
By exploiting the properties of a convolutional dictionary, we present an efficient convolutional analysis dictionary learning approach.
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.
Modeling Semantic Compositionality with Sememe Knowledge
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
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.
Ranked #1 on
Action Recognition
on UTD-MHAD
