Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Ojha, Utkarsh"'
As latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative models fingerprints while ignoring image properties such as semantic content, r
Externí odkaz:
http://arxiv.org/abs/2410.11835
Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering). While broad, their knowledge remains generic (e.g., recognizing a dog), and they are unable to handle per
Externí odkaz:
http://arxiv.org/abs/2406.09400
Text-conditioned image editing has emerged as a powerful tool for editing images. However, in many situations, language can be ambiguous and ineffective in describing specific image edits. When faced with such challenges, visual prompts can be a more
Externí odkaz:
http://arxiv.org/abs/2307.14331
Large language models (LLMs) have made significant advancements in natural language understanding. However, through that enormous semantic representation that the LLM has learnt, is it somehow possible for it to understand images as well? This work i
Externí odkaz:
http://arxiv.org/abs/2306.06094
With generative models proliferating at a rapid rate, there is a growing need for general purpose fake image detectors. In this work, we first show that the existing paradigm, which consists of training a deep network for real-vs-fake classification,
Externí odkaz:
http://arxiv.org/abs/2302.10174
Knowledge distillation aims to transfer useful information from a teacher network to a student network, with the primary goal of improving the student's performance for the task at hand. Over the years, there has a been a deluge of novel techniques a
Externí odkaz:
http://arxiv.org/abs/2205.16004
Autor:
Ojha, Utkarsh, Li, Yijun, Lu, Jingwan, Efros, Alexei A., Lee, Yong Jae, Shechtman, Eli, Zhang, Richard
Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from s
Externí odkaz:
http://arxiv.org/abs/2104.06820
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distribution, which borrows a subset
Externí odkaz:
http://arxiv.org/abs/2104.02052
Publikováno v:
CVPR 2020
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, for mix-and-match image generation. We build upon FineGAN, an unconditio
Externí odkaz:
http://arxiv.org/abs/1911.11758
We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN, and demon
Externí odkaz:
http://arxiv.org/abs/1910.01112