1 citations · 2 across the 10 of their papers we have counts for
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Learning Transferable World-Action Models from Task-Paired Human Videos
Jeongeun Park, Taekyung Kim, Dongyoon Han +1
World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them to rely on appearance shared…
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…
Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han +2
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we…
Token Bottleneck: One Token to Remember Dynamics
Taekyung Kim, Dongyoon Han, Byeongho Heo +2
Deriving compact and temporally aware visual representations from dynamic scenes is essential for successful execution of sequential scene understanding tasks such as visual tracki…
HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts
Wonjae Kim, Sanghyuk Chun, Taekyung Kim +2
In an era where the volume of data drives the effectiveness of self-supervised learning, the specificity and clarity of data semantics play a crucial role in model training. Addres…
Leveraging Temporal Contextualization for Video Action Recognition
Minji Kim, Dongyoon Han, Taekyung Kim +1
We propose a novel framework for video understanding, called Temporally Contextualized CLIP (TC-CLIP), which leverages essential temporal information through global interactions in…
Morphing Tokens Draw Strong Masked Image Models
Taekyung Kim, Byeongho Heo, Dongyoon Han
Masked image modeling (MIM) has emerged as a promising approach for pre-training Vision Transformers (ViTs). MIMs predict masked tokens token-wise to recover target signals that ar…
Learning with Unmasked Tokens Drives Stronger Vision Learners
Taekyung Kim, Sanghyuk Chun, Byeongho Heo +1
Masked image modeling (MIM) has become a leading self-supervised learning strategy. MIMs such as Masked Autoencoder (MAE) learn strong representations by randomly masking input tok…