7 citations · 18 across the 15 of their papers we have counts for
15 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…
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…
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion
Marco Mistretta, Alberto Baldrati, Lorenzo Agnolucci +2
Pre-trained multi-modal Vision-Language Models like CLIP are widely used off-the-shelf for a variety of applications. In this paper, we show that the common practice of individuall…
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation
Marco Mistretta, Alberto Baldrati, Marco Bertini +1
Vision-Language Models (VLMs) demonstrate remarkable zero-shot generalization to unseen tasks, but fall short of the performance of supervised methods in generalizing to downstream…
iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval
Lorenzo Agnolucci, Alberto Baldrati, Alberto Del Bimbo +1
Given a query consisting of a reference image and a relative caption, Composed Image Retrieval (CIR) aims to retrieve target images visually similar to the reference one while inco…
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
Alberto Baldrati, Davide Morelli, Marcella Cornia +2
Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay betwe…
Mapping Memes to Words for Multimodal Hateful Meme Classification
Giovanni Burbi, Alberto Baldrati, Lorenzo Agnolucci +2
Multimodal image-text memes are prevalent on the internet, serving as a unique form of communication that combines visual and textual elements to convey humor, ideas, or emotions.…
Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval
Alberto Baldrati, Marco Bertini, Tiberio Uricchio +1
Given the recent advances in multimodal image pretraining where visual models trained with semantically dense textual supervision tend to have better generalization capabilities th…
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
Giuseppe Cartella, Alberto Baldrati, Davide Morelli +3
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automat…
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features
Alberto Baldrati, Marco Bertini, Tiberio Uricchio +1
Given a query composed of a reference image and a relative caption, the Composed Image Retrieval goal is to retrieve images visually similar to the reference one that integrates th…
ECO: Ensembling Context Optimization for Vision-Language Models
Lorenzo Agnolucci, Alberto Baldrati, Francesco Todino +3
Image recognition has recently witnessed a paradigm shift, where vision-language models are now used to perform few-shot classification based on textual prompts. Among these, the C…
LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On
Davide Morelli, Alberto Baldrati, Giuseppe Cartella +3
The rapidly evolving fields of e-commerce and metaverse continue to seek innovative approaches to enhance the consumer experience. At the same time, recent advancements in the deve…
Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing
Alberto Baldrati, Davide Morelli, Giuseppe Cartella +3
Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the huma…
Zero-Shot Composed Image Retrieval with Textual Inversion
Alberto Baldrati, Lorenzo Agnolucci, Marco Bertini +1
Composed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption that describes the difference between the two i…