3 papers
cs.CV2026
Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference
Hyeonwoo Cho, Donghyeon Baek, Yewon Kim +1
Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computational complexity arising from t…
cs.CV2025
GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
Hyunju Lee, Youngmin Oh, Jeimin Jeon +2
Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train…
cs.CV2025
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Jeimin Jeon, Youngmin Oh, Junghyup Lee +4
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training stra…