mlsanityData to 7 Oct 2026

Camera-Incremental Object Re-Identification with Identity Knowledge Evolution

arXiv 2305.15909 ↗First posted 25 May 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Object Re-identification (ReID) aims to retrieve the probe object from many gallery images with the ReID model inferred based on a stationary camera-free dataset by associating and collecting the identities across all camera views. When deploying the ReID algorithm in real-world scenarios, the aspect of storage, privacy constraints, and dynamic changes of cameras would degrade its generalizability and applicability. Treating each camera's data independently, we introduce a novel ReID task named Camera-Incremental Object Re-identification (CIOR) by continually optimizing the ReID mode from the incoming stream of the camera dataset. Since the identities under different camera views might describe the same object, associating and distilling the knowledge of common identities would boost the discrimination and benefit from alleviating the catastrophic forgetting. In this paper, we propose a novel Identity Knowledge Evolution (IKE) framework for CIOR, consisting of the Identity Knowledge Association (IKA), Identity Knowledge Distillation (IKD), and Identity Knowledge Update (IKU). IKA is proposed to discover the common identities between the current identity and historical identities. IKD has applied to distillate historical identity knowledge from common identities and quickly adapt the historical model to the current camera view. After each camera has been trained, IKU is applied to continually expand the identity knowledge by combining the historical and current identity memories. The evaluation of Market-CL and Veri-CL shows the Identity Knowledge Evolution (IKE) effectiveness for CIOR. code:https://github.com/htyao89/Camera-Incremental-Object-ReID

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 43 of its 79 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

43 of 79 references.

References · 43

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  5. 2016Learning without Forgettingcited by 2,465
  6. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  7. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  8. 2017Continual Learning with Deep Generative Replaycited by 1,152
  9. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,094
  10. 2018Efficient Lifelong Learning with A-GEMcited by 1,044
  11. 2017PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruningcited by 882
  12. 2018Overcoming catastrophic forgetting with hard attention to the taskcited by 672
31 more
  1. 2017Person Transfer GAN to Bridge Domain Gap for Person Re-Identificationcited by 479
  2. 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 446
  3. 2021TransReID: Transformer-based Object Re-Identificationcited by 432
  4. 2019Bag of Tricks and A Strong Baseline for Deep Person Re-identificationcited by 429
  5. 2020Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identificationcited by 214
  6. 2020Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-IDcited by 212
  7. 2017Beyond triplet loss: a deep quadruplet network for person re-identificationcited by 172
  8. 2019A Strong Baseline and Batch Normalization Neck for Deep Person Re-identificationcited by 154
  9. 2020SS-IL: Separated Softmax for Incremental Learningcited by 126
  10. 2019Relation-Aware Global Attention for Person Re-identificationcited by 124
  11. 2021Cluster Contrast for Unsupervised Person Re-Identificationcited by 89
  12. 2019Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identificationcited by 83
  13. 2019Mixed High-Order Attention Network for Person Re-Identificationcited by 65
  14. 2021Group-aware Label Transfer for Domain Adaptive Person Re-identificationcited by 58
  15. 2021Lifelong Person Re-Identification via Adaptive Knowledge Accumulationcited by 57
  16. 2019Self-training with progressive augmentation for unsupervised cross-domain person re-identificationcited by 54
  17. 2019Unsupervised Person Re-identification by Soft Multilabel Learningcited by 53
  18. 2020Parsing-based View-aware Embedding Network for Vehicle Re-Identificationcited by 53
  19. 2021IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDcited by 51
  20. 2021Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identificationcited by 45
  21. 2019Second-order Non-local Attention Networks for Person Re-identificationcited by 38
  22. 2022Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptationcited by 33
  23. 2022Lifelong Person Re-Identification via Knowledge Refreshing and Consolidationcited by 26
  24. 2020Continual Representation Learning for Biometric Identificationcited by 25
  25. 2020Multi-Domain Learning and Identity Mining for Vehicle Re-Identificationcited by 18
  26. 2021Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptationcited by 18
  27. 2020Domain Adaptive Person Re-Identification via Coupling Optimizationcited by 13
  28. 2022Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edgescited by 12
  29. 2022A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identificationcited by 11
  30. 2017Large Margin Learning in Set to Set Similarity Comparison for Person Re-identificationcited by 7
  31. 2019Domain Adaptive Attention Learning for Unsupervised Person Re-Identificationcited by 7

Built on by

Not cited yet.