activity
20182022
most citedCreative Sketch Generation

21 citations · 29 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV20222 cited

Hyperbolic Contrastive Learning for Visual Representations beyond Objects

Songwei Ge, Shlok Mishra, Simon Kornblith +2

Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this pap…

cs.CV2022

MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration

Thomas Hayes, Songyang Zhang, Xi Yin +6

Multimodal video-audio-text understanding and generation can benefit from datasets that are narrow but rich. The narrowness allows bite-sized challenges that the research community…

cs.CL20214 cited

Visual Conceptual Blending with Large-scale Language and Vision Models

Songwei Ge, Devi Parikh

We ask the question: to what extent can recent large-scale language and image generation models blend visual concepts? Given an arbitrary object, we identify a relevant object and…

cs.CV202021 cited

Creative Sketch Generation

Songwei Ge, Vedanuj Goswami, C. Lawrence Zitnick +1

Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that…

cs.GR2020

Learned Interpolation for 3D Generation

Austin Dill, Songwei Ge, Eunsu Kang +2

In order to generate novel 3D shapes with machine learning, one must allow for interpolation. The typical approach for incorporating this creative process is to interpolate in a le…

cs.LG20192 cited

Getting Topology and Point Cloud Generation to Mesh

Austin Dill, Chun-Liang Li, Songwei Ge +1

In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface,…