The core message of this paper is to propose a novel camera-aware Jaccard (CA-Jaccard) distance metric that leverages camera information to enhance the reliability of Jaccard distance for person re-identification tasks.
The core message of this paper is to propose a novel silhouette-driven contrastive learning framework, termed SiCL, for unsupervised long-term person re-identification with clothes change. SiCL incorporates both person silhouette information and hierarchical neighbor structure into a contrastive learning framework to guide the model for learning cross-clothes invariance features.
This paper proposes MLLMReID, a novel approach that leverages multimodal large language models (MLLM) for person re-identification (ReID) tasks. It introduces Common Instruction to simplify the instruction design process and avoid overfitting, and DirectReID to effectively utilize the latent image feature vectors output by the LLM, directly optimizing the visual encoder for improved person feature extraction.
A deep ensemble learning framework that leverages both CNN and Transformer architectures to generate robust feature representations for occluded person re-identification.
Die Einführung von Spatial Cascaded Clustering und Weighted Memory verbessert die Genauigkeit und Effektivität von partbasierten Methoden für die unüberwachte Personenerkennung.