12 citations · 32 across the 16 of their papers we have counts for
17 papers
VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Junxiang Xu, Ruisi Wang, Fanyi Pu +49
Native visual reasoning treats visual generation as the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be understood or outputs to be r…
VOID: Video Object and Interaction Deletion
Saman Motamed, William Harvey, Benjamin Klein +3
Existing video object removal methods excel at inpainting content "behind" the object and correcting appearance-level artifacts such as shadows and reflections. However, when the r…
TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility
Saman Motamed, Minghao Chen, Luc Van Gool +1
Despite impressive visual fidelity, modern video generative models frequently produce sequences that violate intuitive physical laws, such as objects floating, teleporting, or morp…
Do generative video models understand physical principles?
Saman Motamed, Laura Culp, Kevin Swersky +2
AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn "world…
InTraGen: Trajectory-controlled Video Generation for Object Interactions
Zuhao Liu, Aleksandar Yanev, Ahmad Mahmood +7
Advances in video generation have significantly improved the realism and quality of created scenes. This has fueled interest in developing intuitive tools that let users leverage v…
Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models
Saman Motamed, Wouter Van Gansbeke, Luc Van Gool
With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, m…
A Unified and Interpretable Emotion Representation and Expression Generation
Reni Paskaleva, Mykyta Holubakha, Andela Ilic +3
Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are often compound, e.g. happily surprised, and can be mapped to the act…
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment
Lin Zhang, Linghan Xu, Saman Motamed +2
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled tar…
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention
Jianjin Xu, Saman Motamed, Praneetha Vaddamanu +4
Face inpainting is important in various applications, such as photo restoration, image editing, and virtual reality. Despite the significant advances in face generative models, ens…
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models
Saman Motamed, Danda Pani Paudel, Luc Van Gool
Text-to-Image (T2I) models excel at synthesizing concepts such as nouns, appearances, and styles. To enable customized content creation based on a few example images of a concept,…
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
Saman Motamed, Jianjin Xu, Chen Henry Wu +1
Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. Ho…
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Chen Henry Wu, Saman Motamed, Shaunak Srivastava +1
Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a pa…
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
Saman Motamed, Farzad Khalvati
Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image…
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
Saman Motamed, Farzad Khalvati
From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. Wit…
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Saman Motamed, Patrik Rogalla, Farzad Khalvati
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results…
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Saman Motamed, Patrik Rogalla, Farzad Khalvati
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques impr…
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
Saman Motamed, Isha Gujrathi, Dominik Deniffel +3
The segmentation of prostate whole gland and transition zone in Diffusion Weighted MRI (DWI) are the first step in designing computer-aided detection algorithms for prostate cancer…