4 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Understanding and Exploiting Diagonal Attention Sparsity in Autoregressive Image Generation
Daeun Kim, Junwha Hong, Changhun Oh +3
Autoregressive image generation has emerged as a paradigm for multimodal AI systems due to its compatibility with transformer-based LLM serving infrastructures. However, generating…
Isotropic Embedding Perturbations for Robust Vision Language Encoders
Hyesong Choi, Daeun Kim, Song Park +5
Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer stro…
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models
Hyesong Choi, Daeun Kim, Sungmin Cha +2
In this work, we dive deep into the impact of additive noise in pre-training deep networks. While various methods have attempted to use additive noise inspired by the success of la…
CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics
Jinwoo Hwang, Minsu Kim, Daeun Kim +5
Modern retrospective analytics systems leverage cascade architecture to mitigate bottleneck for computing deep neural networks (DNNs). However, the existing cascades suffer two lim…