Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification

73 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “Attributes are also widely used for person re-identification in images or videos, combining features from human face and body appearance [33, 34, 53], especially effective when faces are not fully visible or too small.”
Human Semantic Parsing for Person Re-identification (2018, background): “Also, there has been some attempts [33, 35] to improve person re-identification performance using person attributes.”
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021, methodology): “Face attribute recognition has been applied as a subcomponent to other computer vision tasks such as face verification [31] and person re-idenfication [33, 34, 55].”
Pose-Driven Deep Convolutional Model for Person Re-identification (2017, methodology): “The compared methods include distance metric learning ones: MLAPG [28], LOMO + XQDA [27], BoW [63], WARCA [22] and LDNS [59], and deep learning based ones: IDLA [1], DGDropout [53], SI+CI [48], Gate SCNN [46], LSTM S-CNN [47], MTL-LORAE [41] and Spindle [16].”
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval (2017, background): “Person Re-ID is commonly solved as a classification or distance metric learning problem [1, 15, 18, 24, 27, 36, 40, 41], which suffer from massive computations of classifier training and the high time complexity for online pairwise matching.”