2 citations · 2 across the 10 of their papers we have counts for
10 papers
GPTNT: Benchmarking Real-Time Collaboration Between Multimodal Agents on Keep Talking And Nobody Explodes
Amit Parekh, Sabrina McCallum, Kareem Al-Hasan +3
Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents. While existing benchmarks show that they possess the fundamental…
Retrievit: In-context Retrieval Capabilities of Transformers, State Space Models, and Hybrid Architectures
Georgios Pantazopoulos, Malvina Nikandrou, Ioannis Konstas +1
Transformers excel at in-context retrieval but suffer from quadratic complexity with sequence length, while State Space Models (SSMs) offer efficient linear-time processing but hav…
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users
Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos +5
This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify…
CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts
Malvina Nikandrou, Georgios Pantazopoulos, Nikolas Vitsakis +2
As Vision and Language models (VLMs) are reaching users across the globe, assessing their cultural understanding has become a critical challenge. In this paper, we introduce CROPE,…
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language Modeling
Georgios Pantazopoulos, Malvina Nikandrou, Alessandro Suglia +2
This study explores replacing Transformers in Visual Language Models (VLMs) with Mamba, a recent structured state space model (SSM) that demonstrates promising performance in seque…
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation
Malvina Nikandrou, Georgios Pantazopoulos, Ioannis Konstas +1
Continual learning focuses on incrementally training a model on a sequence of tasks with the aim of learning new tasks while minimizing performance drop on previous tasks. Existing…
Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbreak Attacks
Georgios Pantazopoulos, Amit Parekh, Malvina Nikandrou +1
Augmenting Large Language Models (LLMs) with image-understanding capabilities has resulted in a boom of high-performing Vision-Language models (VLMs). While studying the alignment…
Multitask Multimodal Prompted Training for Interactive Embodied Task Completion
Georgios Pantazopoulos, Malvina Nikandrou, Amit Parekh +6
Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and ob…
Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment
Lu Yu, Malvina Nikandrou, Jiali Jin +1
Automated image captioning has the potential to be a useful tool for people with vision impairments. Images taken by this user group are often noisy, which leads to incorrect and e…
Going for GOAL: A Resource for Grounded Football Commentaries
Alessandro Suglia, José Lopes, Emanuele Bastianelli +6
Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both…