4 papers
UltraViT: Latency-Optimized On-device Vision Encoder for Large Vision-Language Models
Ioannis Maniadis Metaxas, Adrian Bulat, Alberto Baldrati +4
Large Vision-Language Models (LVLMs) remain bottlenecked by massive computational footprints, precluding their deployment on resource-constrained edge devices. While efforts to com…
VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions
Adrian Bulat, Alberto Baldrati, Ioannis Maniadis Metaxas +2
Existing approaches for improving the efficiency of Large Vision-Language Models (LVLMs) are largely based on the concept of visual token reduction. This approach, however, creates…
More Images, More Problems? A Controlled Analysis of VLM Failure Modes
Anurag Das, Adrian Bulat, Alberto Baldrati +4
Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored…
VladVA: Discriminative Fine-tuning of LVLMs
Yassine Ouali, Adrian Bulat, Alexandros Xenos +4
Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models ha…