10 papers
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…
An Efficient Training Pipeline for Reasoning Graphical User Interface Agents
Georgios Pantazopoulos, Eda B. Özyiğit
Visual grounding is the task of localising image regions from natural language queries and is critical for reasoning capable Graphical User Interface agents. Many existing methods…
Towards Understanding Visual Grounding in Visual Language Models
Georgios Pantazopoulos, Eda B. Özyiğit
Visual grounding refers to the ability of a model to identify a region within some visual input that matches a textual description. Consequently, a model equipped with visual groun…
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…
Lost in Space: Probing Fine-grained Spatial Understanding in Vision and Language Resamplers
Georgios Pantazopoulos, Alessandro Suglia, Oliver Lemon +1
An effective method for combining frozen large language models (LLM) and visual encoders involves a resampler module that creates a `visual prompt' which is provided to the LLM, al…
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…