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Occluded person re-identification with deep learning: a survey and perspectives
Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent
surveillance systems. Widespread occlusion significantly impacts the performance of person …
surveillance systems. Widespread occlusion significantly impacts the performance of person …
Diverse embedding expansion network and low-light cross-modality benchmark for visible-infrared person re-identification
For the visible-infrared person re-identification (VIReID) task, one of the major challenges is
the modality gaps between visible (VIS) and infrared (IR) images. However, the training …
the modality gaps between visible (VIS) and infrared (IR) images. However, the training …
Fmcnet: Feature-level modality compensation for visible-infrared person re-identification
Abstract For Visible-Infrared person Re-IDentification (VI-ReID), existing modality-specific
information compensation based models try to generate the images of missing modality from …
information compensation based models try to generate the images of missing modality from …
Shape-erased feature learning for visible-infrared person re-identification
Due to the modality gap between visible and infrared images with high visual ambiguity,
learning diverse modality-shared semantic concepts for visible-infrared person re …
learning diverse modality-shared semantic concepts for visible-infrared person re …
Learning memory-augmented unidirectional metrics for cross-modality person re-identification
This paper tackles the cross-modality person re-identification (re-ID) problem by
suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images …
suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images …
Channel augmented joint learning for visible-infrared recognition
This paper introduces a powerful channel augmented joint learning strategy for the visible-
infrared recognition problem. For data augmentation, most existing methods directly adopt …
infrared recognition problem. For data augmentation, most existing methods directly adopt …
Unsupervised misaligned infrared and visible image fusion via cross-modality image generation and registration
Recent learning-based image fusion methods have marked numerous progress in pre-
registered multi-modality data, but suffered serious ghosts dealing with misaligned multi …
registered multi-modality data, but suffered serious ghosts dealing with misaligned multi …
Partmix: Regularization strategy to learn part discovery for visible-infrared person re-identification
Modern data augmentation using a mixture-based technique can regularize the models from
overfitting to the training data in various computer vision applications, but a proper data …
overfitting to the training data in various computer vision applications, but a proper data …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …