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Found 10 papers, 0 papers with code
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model
Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time.
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
The advantage of Maformer is that it can leverage the MSFA information and non-local dependencies present in the data.
How Image Generation Helps Visible-to-Infrared Person Re-Identification?
To this end, we propose Flow2Flow, a unified framework that could jointly achieve training sample expansion and cross-modality image generation for V2I person ReID.
Towards Complete-View and High-Level Pose-based Gait Recognition
Extensive experiments on two large gait recognition datasets, i. e., CASIA-B and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and existing pose-based methods by a large margin.
Multi-Granularity Graph Pooling for Video-based Person Re-Identification
To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods.
Pose-Aided Video-based Person Re-Identification via Recurrent Graph Convolutional Network
Finally, we propose a dual-attention method consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame.
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising
Based on this fact, we propose a novel TV regularization to simultaneously characterize the sparsity and low-rank priors of the gradient map (LRSTV).
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations.
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively.
Hyperspectral Image Denoising with Log-Based Robust PCA
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs).
