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Found 21 papers, 11 papers with code
Tensor Decomposition based Personalized Federated Learning
Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data.
3D Face Parsing via Surface Parameterization and 2D Semantic Segmentation Network
The topological disk-like 2D face image containing spatial and textural information is transformed from the sampled 3D face data through the face parameterization algorithm, and a specific 2D network called CPFNet is proposed to achieve the semantic segmentation of the 2D parameterized face data with multi-scale technologies and feature aggregation.
Light Field Depth Estimation Based on Stitched-EPI
Depth estimation is one of the most essential problems for light field applications.
Using calibrator to improve robustness in Machine Reading Comprehension
Machine Reading Comprehension(MRC) has achieved a remarkable result since some powerful models, such as BERT, are proposed.
Content-aware Warping for View Synthesis
To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network.
DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval
Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality.
Ranked #1 on
Text based Person Retrieval
on RSTPReid
Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines
In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpolation, to replace the commonly-used geometry warping operation.
Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields
Experimental results on synthetic data show that our method can significantly shrink the performance gap between the previous unsupervised method and supervised ones, and produce depth maps with comparable accuracy to traditional methods with obviously reduced computational cost.
Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses
Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy.
Performance Comparison between Reconfigurable Intelligent Surface and Relays: Theoretical Methods and a Perspective from Operator
On the other hand, a relay node in a traditional relay network has to be active, which indicates that it will consume energy when it is relaying the signal or information between the source and destination nodes.
Information Theory Information Theory
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization
The main idea of our method is that the KD technique is leveraged to transfer the knowledge from a "teacher" model to a "student" model when exploiting LSQ to quantize that "student" model during the quantization training process.
BinaryBERT: Pushing the Limit of BERT Quantization
In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization.
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution
To preserve the parallax structure among the reconstructed SAIs, we subsequently append a consistency regularization network trained over a structure-aware loss function to refine the parallax relationships over the coarse estimation.
Deep Spatial-angular Regularization for Compressive Light Field Reconstruction over Coded Apertures
Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms.
Image and Video Processing
Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension.
Learning Light Field Angular Super-Resolution via a Geometry-Aware Network
In addition, our method preserves the light field parallax structure better.
Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer
The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0. 64, while the one built with DL-based features yielded the mean AUC of 0. 73.
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion
Specifically, the coarse sub-aperture image (SAI) synthesis module first explores the scene geometry from an unstructured sparsely-sampled LF and leverages it to independently synthesize novel SAIs, in which a confidence-based blending strategy is proposed to fuse the information from different input SAIs, giving an intermediate densely-sampled LF.
Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses
To the best of our knowledge, this is the first end-to-end deep learning method for reconstructing a high-resolution LF image with a hybrid input.
HAMLET: Interpretable Human And Machine co-LEarning Technique
Although cEEG monitoring yields large volumes of data, labeling costs and difficulty make it hard to build a classifier.
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).
