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Found 33 papers, 10 papers with code
Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network
Existing hyperspectral image (HSI) super-resolution (SR) methods struggle to effectively capture the complex spectral-spatial relationships and low-level details, while diffusion models represent a promising generative model known for their exceptional performance in modeling complex relations and learning high and low-level visual features.
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment
Firstly, we devise a new diffusion restoration network that leverages the produced enhanced image and noise-containing images, incorporating nonlinear features obtained during the denoising process of the diffusion model, as high-level visual information.
Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains.
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration
We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation.
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving.
One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
In this paper, we propose an effective inlier estimation method for unsupervised point cloud registration by capturing geometric structure consistency between the source point cloud and its corresponding reference point cloud copy.
Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage.
Ranked #2 on
Change Detection
on DSIFN-CD
Building change detection for remote sensing images
Change Detection
+1
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration
However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm.
Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification
In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional network (GCN) based models have shown impressive performance.
Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach
In this paper, we propose a novel Multiview Variational Graph Information Bottleneck (MVGIB) principle to maximize the agreement for common representations and the disagreement for view-specific representations.
Entropy Induced Pruning Framework for Convolutional Neural Networks
By leveraging the proposed AFIE, the proposed framework is able to yield a stable importance evaluation of each filter no matter whether the original model is trained fully.
SBPF: Sensitiveness Based Pruning Framework For Convolutional Neural Network On Image Classification
Therefore, we propose a sensitiveness based method to evaluate the importance of each layer from the perspective of inference accuracy by adding extra damage for the original model.
Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism
The modeling of multi-view point cloud registration as multi-task optimization are twofold.
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution.
Audio-visual Representation Learning for Anomaly Events Detection in Crowds
The second is an audio CNN for encoding Log Mel-Spectrogram of audio signals.
Self-adaptive Multi-task Particle Swarm Optimization
In the focus search strategy, if there is no knowledge source benefit the optimization of a task, then all knowledge sources in the task's pool are forbidden to be utilized except the task, which helps to improve the performance of the proposed algorithm.
Hierarchical Multimodal Transformer to Summarize Videos
To integrate the two kinds of information, they are encoded in a two-stream scheme, and a multimodal fusion mechanism is developed based on the hierarchical transformer.
Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer
To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes.
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
AudioVisual Video Summarization
Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this.
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs.
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images
Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples.
CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification
For each feature map, the channel attention module is proposed to explore channel-wise correlation.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Multi-Party Dual Learning
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem.
Feature Weighted Non-negative Matrix Factorization
Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis.
AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy
Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD).
Differentially Private ADMM Algorithms for Machine Learning
For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with performance guarantee of $(\epsilon,\delta)$-differential privacy ($(\epsilon,\delta)$-DP).
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer's readout to form a global context-aware node representation.
Nucleus Neural Network: A Data-driven Self-organized Architecture
In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method.
Semi-supervised Multimodal Hashing
As unsupervised multimodal hashing methods are usually inferior to supervised ones, while the supervised ones requires too much manually labeled data, the proposed method in this paper utilizes a part of labels to design a semi-supervised multimodal hashing method.
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
Human activity recognition (HAR) has become a popular topic in research because of its wide application.
