14 citations · 27 across the 11 of their papers we have counts for
13 papers
VNODE: A Piecewise Continuous Volterra Neural Network
Siddharth Roheda, Aniruddha Bala, Rohit Chowdhury +1
This paper introduces Volterra Neural Ordinary Differential Equations (VNODE), a piecewise continuous Volterra Neural Network that integrates nonlinear Volterra filtering with cont…
Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing
Rohit Chowdhury, Aniruddha Bala, Rohan Jaiswal +1
The rapid progress of image-to-video (I2V) generation models has introduced significant risks by enabling deceptive or malicious video synthesis from a single image. Prior defenses…
DCT-Shield: A Robust Frequency Domain Defense against Malicious Image Editing
Aniruddha Bala, Rohit Chowdhury, Rohan Jaiswal +1
Advancements in diffusion models have enabled effortless image editing via text prompts, raising concerns about image security. Attackers with access to user images can exploit the…
LLVD: LSTM-based Explicit Motion Modeling in Latent Space for Blind Video Denoising
Loay Rashid, Siddharth Roheda, Amit Unde
Video restoration plays a pivotal role in revitalizing degraded video content by rectifying imperfections caused by various degradations introduced during capturing (sensor noise,…
GalaxyEdit: Large-Scale Image Editing Dataset with Enhanced Diffusion Adapter
Aniruddha Bala, Rohan Jaiswal, Siddharth Roheda +2
Training of large-scale text-to-image and image-to-image models requires a huge amount of annotated data. While text-to-image datasets are abundant, data available for instruction-…
CART: Compositional Auto-Regressive Transformer for Image Generation
Siddharth Roheda, Rohit Chowdhury, Aniruddha Bala +1
We propose a novel Auto-Regressive (AR) image generation approach that models images as hierarchical compositions of interpretable visual layers. While AR models have achieved tran…
Fast OT for Latent Domain Adaptation
Siddharth Roheda, Ashkan Panahi, Hamid Krim
In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is…
Latent Code-Based Fusion: A Volterra Neural Network Approach
Sally Ghanem, Siddharth Roheda, Hamid Krim
We propose a deep structure encoder using the recently introduced Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly cap…
Volterra Neural Networks (VNNs)
Siddharth Roheda, Hamid Krim
The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the com…
Robust Multi-Modal Sensor Fusion: An Adversarial Approach
Siddharth Roheda, Hamid Krim, Benjamin S. Riggan
In recent years, multi-modal fusion has attracted a lot of research interest, both in academia, and in industry. Multimodal fusion entails the combination of information from a set…
Event Driven Fusion
Siddharth Roheda, Hamid Krim, Zhi-Quan Luo +1
This paper presents a technique which exploits the occurrence of certain events as observed by different sensors, to detect and classify objects. This technique explores the extent…
Decision Level Fusion: An Event Driven Approach
Siddharth Roheda, Hamid Krim, Zhi-Quan Luo +1
This paper presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores t…
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks
Siddharth Roheda, Benjamin S. Riggan, Hamid Krim +1
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i.e. transferring) knowledge from sensor data and enhancing low-resolution targ…