activity
20172026
most cited2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network

8 citations · 12 across the 15 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2026

Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation

Tobia Poppi, Silvia Cappelletti, Sara Sarto +5

Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interven…

cs.CV2026

CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models

Tobia Poppi, Burak Uzkent, Amanmeet Garg +7

Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actions and temporal order. Existing mitiga…

cs.CV2025

What Happens Next? Next Scene Prediction with a Unified Video Model

Xinjie Li, Zhimin Chen, Rui Zhao +3

Recent unified models for joint understanding and generation have significantly advanced visual generation capabilities. However, their focus on conventional tasks like text-to-vid…

cs.CV2023

Stochastic Light Field Holography

Florian Schiffers, Praneeth Chakravarthula, Nathan Matsuda +5

The Visual Turing Test is the ultimate goal to evaluate the realism of holographic displays. Previous studies have focused on addressing challenges such as limited étendue and imag…

cs.CV20221 cited

medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space

Amil Dravid, Florian Schiffers, Boqing Gong +1

Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space…

cs.CV2022

Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes

Amil Dravid, Florian Schiffers, Yunan Wu +2

Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But th…