DRAPE generates query-image conditioned prompts on the fly for multimodal continual instruction tuning and reports SOTA results on MCIT benchmarks.
Dynamic mixture of curriculum lora experts for continual multimodal instruction tuning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
other 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Hystar adapts CLIP-like models to unseen query styles by generating per-input singular-value perturbations with a hypernetwork for attention layers and a new StyleNCE contrastive loss.
CRAFT is a continual learning method for LLMs that learns low-rank interventions on hidden representations, using a unified KL-divergence objective to handle task routing by output divergence, forgetting control via prior-state regularization, and intervention merging.
citing papers explorer
-
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
DRAPE generates query-image conditioned prompts on the fly for multimodal continual instruction tuning and reports SOTA results on MCIT benchmarks.
-
Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation
Hystar adapts CLIP-like models to unseen query styles by generating per-input singular-value perturbations with a hypernetwork for attention layers and a new StyleNCE contrastive loss.
-
CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning
CRAFT is a continual learning method for LLMs that learns low-rank interventions on hidden representations, using a unified KL-divergence objective to handle task routing by output divergence, forgetting control via prior-state regularization, and intervention merging.