An investigation into the use of ConvNext within IICS/IIDS framework for person Re-ID

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In this thesis, we explore the integration of ConvNeXt, a CNN-based network inspired by vision transformers, into the Intra and Inter Camera Similarity (IICS) and Intra and Inter Domain Similarity (IIDS) frameworks for unsupervised person Re-ID. Building upon IICS/IIDS framework that generates pseudo labels through intra and inter stages and utilizing techniques such as Adaptive Instance and Batch Normalization (AIBN) and Transform Normalization (TNorm) to minimize intra-camera and inter-camera variations respectively, our work emphasizes the application of ConvNeXt as a feature extractor. ConvNeXt gets higher mAP and CMC on the Market1501 and MSMT17 datasets than most unsupervised learning methods. Furthermore, we explored the effect of AIBN and TNorm normalization techniques in ConvNeXt. We showed their effectiveness in reducing intra-camera and inter-camera variations if AIBN is inserted in the final stages (Stage 3 and stage 4) and TNorm layers are included after stage 1, stage 2, and stage 3. We also examined the effects of four ConvNeXt variants within the IICS/IIDS framework, emphasizing the advantages of using larger variants of ConvNeXt as a feature extractor for person Re-ID.
Pedestrian identification, Unsupervised learning, Pseudo labels, Computer vision, Deep learning