2 citations · 5 across the 7 of their papers we have counts for
7 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…
Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancing
Ioannis Maniadis Metaxas, Georgios Tzimiropoulos, Ioannis Patras
Self-supervised learning has recently emerged as the preeminent pretraining paradigm across and between modalities, with remarkable results. In the image domain specifically, group…
Aligned Unsupervised Pretraining of Object Detectors with Self-training
Ioannis Maniadis Metaxas, Adrian Bulat, Ioannis Patras +2
The unsupervised pretraining of object detectors has recently become a key component of object detector training, as it leads to improved performance and faster convergence during…