1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
Seeing What You Say: Expressive Image Generation from Speech
Jiyoung Lee, Song Park, Sanghyuk Chun +1
This paper proposes VoxStudio, the first unified and end-to-end speech-to-image model that generates expressive images directly from spoken descriptions by jointly aligning linguis…
DNNs May Determine Major Properties of Their Outputs Early, with Timing Possibly Driven by Bias
Song Park, Sanghyuk Chun, Byeongho Heo +1
This paper argues that deep neural networks (DNNs) mostly determine their outputs during the early stages of inference, where biases inherent in the model play a crucial role in sh…
Probabilistic Language-Image Pre-Training
Sanghyuk Chun, Wonjae Kim, Song Park +1
Vision-language models (VLMs) embed aligned image-text pairs into a joint space but often rely on deterministic embeddings, assuming a one-to-one correspondence between images and…
Rotary Position Embedding for Vision Transformer
Byeongho Heo, Song Park, Dongyoon Han +1
Rotary Position Embedding (RoPE) performs remarkably on language models, especially for length extrapolation of Transformers. However, the impacts of RoPE on computer vision domain…