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Found 37 papers, 7 papers with code
ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices
The matrix composed of basis vectors is referred to as the proxy matrix, and auxiliary variables serve as the coefficients of this linear combination.
Learning Memory Augmented Cascading Network for Compressed Sensing of Images
The IDR module is designed to reconstruct the remaining details from the residual measurement vector, and MRU is employed to update the residual measurement vector and feed it into the next IDR module.
PointAttN: You Only Need Attention for Point Cloud Completion
To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for processing point cloud in a per-point manner to eliminate kNNs.
Adaptively Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation
MFT fuses the features of a varying number of views with a relative-attention block.
DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance
This dense correlation volumes enables the network to accurately discover the structured pair-wise pixel similarities among the common salient objects.
Deep Transport Network for Unsupervised Video Object Segmentation
The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network.
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction
Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period.
Meta-Learning with Network Pruning
To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network.
Hyperspectral Image Classification with Attention Aided CNNs
Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion.
Dual Temporal Memory Network for Efficient Video Object Segmentation
We present an end-to-end network which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VOS.
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy.
Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP).
Deep Object Co-segmentation via Spatial-Semantic Network Modulation
With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image.
Meta-Learning with Network Pruning for Overfitting Reduction
Meta-Learning has achieved great success in few-shot learning.
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands.
Hyperspectral image classification using spectral-spatial LSTMs
Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature.
Robust Subspace Clustering with Compressed Data
Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e. g., clustering) of compressed data.
A New Theory for Matrix Completion
To break through the limits of random sampling, this paper introduces a new hypothesis called \emph{isomeric condition}, which is provably weaker than the assumption of uniform sampling and arguably holds even when the missing data is placed irregularly.
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it.
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
It remains open to explore duality theory and algorithms in such a non-convex and NP-hard problem setting.
Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning
Then, with the updated appearances, we formulate a spatio-temporal graphical model comprised of the superpixel label consistency potentials.
Learning Additive Exponential Family Graphical Models via \ell_{2,1}-norm Regularized M-Estimation
We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms.
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification
In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification.
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification.
Visual Tracking via Boolean Map Representations
In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking.
Self-Paced Multi-Task Learning
In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL).
A Self-Paced Regularization Framework for Multi-Label Learning
In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime.
Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification
In the second step, hypergraph is used to represent the high order relationships between each datum and its prominent samples by regarding them as a hyperedge.
Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval.
Additive Nearest Neighbor Feature Maps
In this paper, we present a concise framework to approximately construct feature maps for nonlinear additive kernels such as the Intersection, Hellinger's, and Chi^2 kernels.
Adaptive Compressive Tracking via Online Vector Boosting Feature Selection
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling.
Robust Visual Tracking via Convolutional Networks
In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.
Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement.
Max-Margin based Discriminative Feature Learning
In this paper, we propose a new max-margin based discriminative feature learning method.
Newton Greedy Pursuit: A Quadratic Approximation Method for Sparsity-Constrained Optimization
The main theme of this type of methods is to evaluate the function gradient in the previous iteration to update the non-zero entries and their values in the next iteration.
