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Found 6 papers, 2 papers with code
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification
Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras.
Cross-Modality Person Re-identification
Image Classification
+4
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation
Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application.
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification
Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning.
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification
The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality.
Cross-Modality Person Re-identification
Person Re-Identification
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification
The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance.
Cross-Modality Person Re-identification
Person Re-Identification
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN).
Ranked #1 on
Denoising
on AAPM
