Dynamic token selection and training only 1.6 million parameters instead of over 300 million reduces computation by 48-55% and improves accuracy over prior state-of-the-art on the NuScenes dataset.
Rethinking Token Reduction With Parameter-Efficient Fine-Tuning in ViT for Pixel-Level Tasks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Efficient Multi-View 3D Object Detection by Dynamic Token Selection and Fine-Tuning
Dynamic token selection and training only 1.6 million parameters instead of over 300 million reduces computation by 48-55% and improves accuracy over prior state-of-the-art on the NuScenes dataset.