39 citations · 159 across the 37 of their papers we have counts for
7 papers · 1 filter
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…
AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models
Seunghoon Lee, Jeongwoo Choi, Byunggwan Son +3
We present in this paper a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models. We show analytically and empirically that quantization errors for…
3DPillars: Pillar-based two-stage 3D object detection
Jongyoun Noh, Junghyup Lee, Hyekang Park +1
PointPillars is the fastest 3D object detector that exploits pseudo image representations to encode features for 3D objects in a scene. Albeit efficient, PointPillars is typically…
Jailbreaking on Text-to-Video Models via Scene Splitting Strategy
Wonjun Lee, Haon Park, Doehyeon Lee +2
Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While recent studies have explored vulnerabilit…
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…
Maximizing the Position Embedding for Vision Transformers with Global Average Pooling
Wonjun Lee, Bumsub Ham, Suhyun Kim
In vision transformers, position embedding (PE) plays a crucial role in capturing the order of tokens. However, in vision transformer structures, there is a limitation in the expre…