15 citations · 53 across the 13 of their papers we have counts for
14 papers
Do Vision Models Develop Human-Like Progressive Difficulty Understanding?
Zeyi Huang, Utkarsh Ojha, Yuyang Ji +2
When a human undertakes a test, their responses likely follow a pattern: if they answered an easy question incorrectly, they would likely answer a more difficult one…
Aligned Datasets Improve Detection of Latent Diffusion-Generated Images
Anirudh Sundara Rajan, Utkarsh Ojha, Jedidiah Schloesser +1
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 fi…
Yo'LLaVA: Your Personalized Language and Vision Assistant
Thao Nguyen, Haotian Liu, Yuheng Li +3
Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering). While broad, their knowledge remain…
Edit One for All: Interactive Batch Image Editing
Thao Nguyen, Utkarsh Ojha, Yuheng Li +2
In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we wan…
Visual Instruction Inversion: Image Editing via Visual Prompting
Thao Nguyen, Yuheng Li, Utkarsh Ojha +1
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 imag…
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
Mu Cai, Zeyi Huang, Yuheng Li +3
Large language models (LLMs) have made significant advancements in natural language understanding. However, through that enormous semantic representation that the LLM has learnt, i…
Towards Universal Fake Image Detectors that Generalize Across Generative Models
Utkarsh Ojha, Yuheng Li, Yong Jae Lee
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, whic…
What Knowledge Gets Distilled in Knowledge Distillation?
Utkarsh Ojha, Yuheng Li, Anirudh Sundara Rajan +2
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…
Few-shot Image Generation via Cross-domain Correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu +4
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 sourc…
Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains
Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee
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…
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…
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh +1
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 s…
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained obj…
NAG: Network for Adversary Generation
Konda Reddy Mopuri, Utkarsh Ojha, Utsav Garg +1
Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifi…