UvA-DARE (Digital Academic Repository) TWIST & SCOUT: Grounding Multimodal LLM-Experts by Forget-Free Tuning
∗. AritraBhowmik, ∗. MohammadMahdiDerakhshani, Dennis C. Koelma et al.
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UvA-DARE (Digital Academic Repository) Elastic ViTs from Pretrained Models without Retraining
Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort et al.
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Quasibinary Classifier for Images with Zero and Multiple Labels
It is shown in a variety of image classification settings and on several datasets, that quasibinary classifiers are considerably better in classification settings where regular binary and softmax classifiers suffer, including zero-label and multi-label classification.
Shuai Liao, E. Gavves, Changyong Oh et al.
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