Robust Ensemble Person Re-Identification with Orthogonal Fusion and Occlusion Handling
A deep ensemble learning framework that leverages both CNN and Transformer architectures to generate robust feature representations for occluded person re-identification.
Part-Attention Based Model Enhances Occluded Person Re-Identification
A novel part-attention based model (PAB-ReID) is proposed to effectively address the challenges in occluded person re-identification by leveraging human parsing labels to generate accurate part attention maps, a fine-grained feature focuser to suppress background interference, and a part triplet loss to learn robust local features.
Prompt-Guided Feature Disentangling for Improving Occluded Person Re-Identification
Leveraging textual prompts and hybrid attention mechanisms to generate well-aligned part features for occluded person re-identification, while preserving pre-trained knowledge to improve generalization.
Data Distribution Reconstruction Network (DDRN) for Improved Occluded Person Re-Identification
This paper introduces DDRN, a novel generative model that enhances occluded person re-identification by reconstructing image features based on learned data distribution, effectively mitigating occlusion and background interference.
© 2024 by Linnk AI