5 papers
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1
Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications. Bayesian methods aim to counter this by learning a d…
ReCap: Lightweight Referential Grounding for Coherent Story Visualization
Aditya Arora, Akshita Gupta, Pau Rodriguez +1
Story Visualization aims to generate a sequence of images that faithfully depicts a textual narrative that preserve character identity, spatial configuration, and stylistic coheren…
Evaluating the Impact of Post-Training Quantization on Reliable VQA with Multimodal LLMs
Paul Jonas Kurz, Tobias Jan Wieczorek, Mohamed A. Abdelsalam +2
Multimodal Large Language Models (MLLM) are increasingly deployed in domains where both reliability and efficiency are critical. However, current models remain overconfident, produ…
Chrono: A Simple Blueprint for Representing Time in MLLMs
Hector Rodriguez, Boris Meinardus, Anil Batra +2
The recent success of Large Language Models (LLMs) has prompted the extension to the multimodal domain, developing image-text Multimodal LLMs (MLLMs) and then video-text models. In…
Predicting Implicit Arguments in Procedural Video Instructions
Anil Batra, Laura Sevilla-Lara, Marcus Rohrbach +1
Procedural texts help AI enhance reasoning about context and action sequences. Transforming these into Semantic Role Labeling (SRL) improves understanding of individual steps by id…