4 accepted papers
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 the dual challenges of coherent image transformation and context preservation. In response our work introduces prompt augme…
Manipulating images by changing only specific attributes has been a long-standing research problem. Existing methods that rely solely on a global generator often suffer from changing unwanted attributes along with the desired attributes. Although hierarchical networks consisting of global and local…
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 applied on several computer vision tasks. Unlike previous works, which are mostly focused on doing augmentation in the af…
Generative Adversarial Networks (GANs) have been used widely to generate large volumes of synthetic data. This data is being utilised for augmenting with real examples in order to train deep Convolutional Neural Networks (CNNs). Studies have shown that the generated examples lack sufficient realism…