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cs.CV2026
Scalable Analytic Classifiers with Associative Drift Compensation for Class-Incremental Learning of Vision Transformers
Xuan Rao, Mingming Ha, Bo Zhao +2
Class-incremental learning (CIL) with Vision Transformers (ViTs) faces a major computational bottleneck during the classifier reconstruction phase, where most existing methods rely…
cs.CV2025
Compensating Distribution Drifts in Class-incremental Learning of Pre-trained Vision Transformers
Xuan Rao, Simian Xu, Zheng Li +4
Recent advances have shown that sequential fine-tuning (SeqFT) of pre-trained vision transformers (ViTs), followed by classifier refinement using approximate distributions of class…
cs.CV2024
Object-Centric Relational Representations for Image Generation
Luca Butera, Andrea Cini, Alberto Ferrante +1
Conditioning image generation on specific features of the desired output is a key ingredient of modern generative models. However, existing approaches lack a general and unified wa…