activity
20182023
most citedSelf-Imitation Learning

140 citations · 283 across the 25 of their papers we have counts for

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Showing cs.CVShow all

7 papers · 1 filter

cs.CV2023

Story Visualization by Online Text Augmentation with Context Memory

Daechul Ahn, Daneul Kim, Gwangmo Song +4

Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a long-…

cs.CV2023★ 21 cited

Scalable 3D Captioning with Pretrained Models

Tiange Luo, Chris Rockwell, Honglak Lee +1

We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects. This approach utilizes pretrained models from image captioning, image-text alignment, and…

cs.CV2022★ 2 cited

Simple and Effective Synthesis of Indoor 3D Scenes

Jing Yu Koh, Harsh Agrawal, Dhruv Batra +6

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including v…

cs.CV2021★ 1 cited

L-Verse: Bidirectional Generation Between Image and Text

Taehoon Kim, Gwangmo Song, Sihaeng Lee +6

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially…

cs.CV2021

Pathdreamer: A World Model for Indoor Navigation

Jing Yu Koh, Honglak Lee, Yinfei Yang +2

People navigating in unfamiliar buildings take advantage of myriad visual, spatial and semantic cues to efficiently achieve their navigation goals. Towards equipping computational…

cs.CV2021★ 8 cited

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

Wonkwang Lee, Whie Jung, Han Zhang +6

Learning to predict the long-term future of video frames is notoriously challenging due to inherent ambiguities in the distant future and dramatic amplifications of prediction erro…

cs.CV2020★ 1 cited

Why Do Better Loss Functions Lead to Less Transferable Features?

Simon Kornblith, Ting Chen, Honglak Lee +1

Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions…