3 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
Prompt Augmentation for Self-supervised Text-guided Image Manipulation
Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim
Text-guided image editing finds applications in various creative and practical fields. While recent studies in image generation have advanced the field, they often struggle with th…
iEdit: Localised Text-guided Image Editing with Weak Supervision
Rumeysa Bodur, Erhan Gundogdu, Binod Bhattarai +3
Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability in the output space of the gen…
A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation
Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim
Editing facial expressions by only changing what we want is a long-standing research problem in Generative Adversarial Networks (GANs) for image manipulation. Most of the existing…
3D Dense Geometry-Guided Facial Expression Synthesis by Adversarial Learning
Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim
Manipulating facial expressions is a challenging task due to fine-grained shape changes produced by facial muscles and the lack of input-output pairs for supervised learning. Unlik…
Sampling Strategies for GAN Synthetic Data
Binod Bhattarai, Seungryul Baek, Rumeysa Bodur +1
Generative Adversarial Networks (GANs) have been used widely to generate large volumes of synthetic data. This data is being utilized for augmenting with real examples in order to…
AugLabel: Exploiting Word Representations to Augment Labels for Face Attribute Classification
Binod Bhattarai, Rumeysa Bodur, Tae-Kyun Kim
Augmenting data in image space (eg. flipping, cropping etc) and activation space (eg. dropout) are being widely used to regularise deep neural networks and have been successfully a…