9 citations · 12 across the 4 of their papers we have counts for
9 papers · 1 filter
Contrastive Learning for Diverse Disentangled Foreground Generation
Yuheng Li, Yijun Li, Jingwan Lu +3
We introduce a new method for diverse foreground generation with explicit control over various factors. Existing image inpainting based foreground generation methods often struggle…
GIRAFFE HD: A High-Resolution 3D-aware Generative Model
Yang Xue, Yuheng Li, Krishna Kumar Singh +1
3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRA…
InsetGAN for Full-Body Image Generation
Anna Frühstück, Krishna Kumar Singh, Eli Shechtman +3
While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities…
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman +3
Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model's generalizabili…
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha +1
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, fo…
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Krishna Kumar Singh, Hao Yu, Aron Sarmasi +2
We propose 'Hide-and-Seek' a general purpose data augmentation technique, which is complementary to existing data augmentation techniques and is beneficial for various visual recog…