1 citations · 1 across the 4 of their papers we have counts for
4 papers
CrowdVLA: Embodied Vision-Language-Action Agents for Context-Aware Crowd Simulation
Juyeong Hwang, Seong-Eun Hong, Jinhyun Kim +4
Crowds do not merely move; they decide. Human navigation is inherently contextual: people interpret the meaning of space, social norms, and potential consequences before acting. Si…
Pri4R: Learning World Dynamics for Vision-Language-Action Models with Privileged 4D Representation
Jisoo Kim, Jungbin Cho, Sanghyeok Chu +9
Humans learn not only how their bodies move, but also how the surrounding world responds to their actions. In contrast, while recent Vision-Language-Action (VLA) models exhibit imp…
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining
Bumsoo Kim, Yeonsik Jo, Jinhyung Kim +1
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web. To enhance data-effic…
Expediting Contrastive Language-Image Pretraining via Self-distilled Encoders
Bumsoo Kim, Jinhyung Kim, Yeonsik Jo +1
Recent advances in vision language pretraining (VLP) have been largely attributed to the large-scale data collected from the web. However, uncurated dataset contains weakly correla…