Search Results for author:
Found 13 papers, 12 papers with code
Null-text Inversion for Editing Real Images using Guided Diffusion Models
Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and prompt editing, showing high-fidelity editing of real images.
Ranked #4 on
Text-based Image Editing
on PIE-Bench
Text-Only Training for Image Captioning using Noise-Injected CLIP
We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images.
Ranked #1 on
Image Captioning
on MSCOCO
Language Modelling
Semi Supervised Learning for Image Captioning
Prompt-to-Prompt Image Editing with Cross Attention Control
Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome.
Ranked #14 on
Text-based Image Editing
on PIE-Bench
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
Self-Distilled StyleGAN: Towards Generation from Internet Photos
To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.
Stitch it in Time: GAN-Based Facial Editing of Real Videos
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing.
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this work, we introduce this approach into the realm of encoder-based inversion.
ClipCap: CLIP Prefix for Image Captioning
Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image.
Ranked #1 on
Image Captioning
on Conceptual Captions
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
To alleviate this problem, we introduce JOKR - a JOint Keypoint Representation that captures the motion common to both the source and target videos, without requiring any object prior or data collection.
Pivotal Tuning for Latent-based Editing of Real Images
The key idea is pivotal tuning - a brief training process that preserves the editing quality of an in-domain latent region, while changing its portrayed identity and appearance.
Masked Based Unsupervised Content Transfer
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
Structural-analogy from a Single Image Pair
In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B.
Mask Based Unsupervised Content Transfer
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
