Presents LLaVA-AlignedVQ, an edge-cloud VQA system with AlignedVQ that delivers 1365x feature compression, 96.8% lower transmission than JPEG90, 2-15x speedup, and accuracy within -2.23% to +1.6% of the baseline across eight datasets.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2024 2representative citing papers
DoRA improves LoRA by decomposing weights into magnitude and direction and updating only direction with low-rank matrices, closing much of the gap to full fine-tuning.
citing papers explorer
-
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models
Presents LLaVA-AlignedVQ, an edge-cloud VQA system with AlignedVQ that delivers 1365x feature compression, 96.8% lower transmission than JPEG90, 2-15x speedup, and accuracy within -2.23% to +1.6% of the baseline across eight datasets.
-
DoRA: Weight-Decomposed Low-Rank Adaptation
DoRA improves LoRA by decomposing weights into magnitude and direction and updating only direction with low-rank matrices, closing much of the gap to full fine-tuning.