244 citations · 251 across the 10 of their papers we have counts for
11 papers · 1 filter
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…
Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions
Ioanna Ntinou, Alexandros Xenos, Yassine Ouali +2
Contrastively-trained Vision-Language Models (VLMs), such as CLIP, have become the standard approach for learning discriminative vision-language representations. However, these mod…
Fwd2Bot: LVLM Visual Token Compression with Double Forward Bottleneck
Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
In this work, we aim to compress the vision tokens of a Large Vision Language Model (LVLM) into a representation that is simultaneously suitable for (a) generative and (b) discrimi…
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…
CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs
Yassine Ouali, Adrian Bulat, Brais Martinez +1
Despite recent successes, LVLMs or Large Vision Language Models are prone to hallucinating details like objects and their properties or relations, limiting their real-world deploym…
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models
Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential o…
Black Box Few-Shot Adaptation for Vision-Language models
Yassine Ouali, Adrian Bulat, Brais Martinez +1
Vision-Language (V-L) models trained with contrastive learning to align the visual and language modalities have been shown to be strong few-shot learners. Soft prompt learning is t…
Spatial Contrastive Learning for Few-Shot Classification
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent r…
Autoregressive Unsupervised Image Segmentation
Yassine Ouali, Céline Hudelot, Myriam Tami
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspirati…
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation. Consistency training has proven to be a powerful semi-supervised learn…