A Convolutional Baseline For Person Re-identification Using Vision And Language Descriptions

Abstract

Classical person re-identification approaches assume that a person of interest has appeared across different cameras and can be queried by one of the existing images. However, in real-world surveillance scenarios, frequently no visual information will be available about the queried person. In such scenarios, a natural language description of the person by a witness will provide the only source of information for retrieval. In this work, person re-identification using both vision and language information is addressed under all possible gallery and query scenarios. A two stream deep convolutional neural network framework supervised by cross entropy loss is presented. The weights connecting the second last layer to the last layer with class probabilities, i.e., logits of softmax layer are shared in both networks. Canonical Correlation Analysis is performed to enhance the correlation between the two modalities in a joint latent embedding space. To investigate the benefits of the proposed a

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyfarooq2020a

Related papers

• Triplet-based Deep Similarity Learning For Person Re-identification (2018)10.61
• Advancing Person Re-identification: Tensor-based Feature Fusion And Multilinear Subspace Learning (2023)2.26
• Interpretable And Generalizable Person Re-identification With Query-adaptive Convolution And Temporal Lifting (2019)20.43
• A Discriminatively Learned CNN Embedding For Person Re-identification (2016)20.32
• A Pose-sensitive Embedding For Person Re-identification With Expanded Cross Neighborhood Re-ranking (2017)23.25
• Cross-entropy Adversarial View Adaptation For Person Re-identification (2019)12.93
• Person Re-identification: Past, Present And Future (2016)0.00
• Ranking And Classification Driven Feature Learning For Person Re_identification (2019)4.01