4 citations · 8 across the 15 of their papers we have counts for
5 papers · 1 filter
GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling
Shivanshu Shekhar, Uttaran Bhattacharya, Raghavendra Addanki +3
Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward modeling, but these models struggle…
Analyzing the Sensitivity of Vision Language Models in Visual Question Answering
Monika Shah, Sudarshan Balaji, Somdeb Sarkhel +2
We can think of Visual Question Answering as a (multimodal) conversation between a human and an AI system. Here, we explore the sensitivity of Vision Language Models (VLMs) through…
On Explaining Visual Captioning with Hybrid Markov Logic Networks
Monika Shah, Somdeb Sarkhel, Deepak Venugopal
Deep Neural Networks (DNNs) have made tremendous progress in multimodal tasks such as image captioning. However, explaining/interpreting how these models integrate visual informati…
Disentangling Fine-Tuning from Pre-Training in Visual Captioning with Hybrid Markov Logic
Monika Shah, Somdeb Sarkhel, Deepak Venugopal
Multimodal systems have highly complex processing pipelines and are pretrained over large datasets before being fine-tuned for specific tasks such as visual captioning. However, it…
SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation
Chen Yi Lu, Md Mehrab Tanjim, Ishita Dasgupta +4
We present SKALD, a multi-shot video assembly method that constructs coherent video sequences from candidate shots with minimal reliance on text. Central to our approach is the Lea…