478 citations · 905 across the 72 of their papers we have counts for
10 papers · 1 filter
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Samuele Poppi, Tobia Poppi, Federico Cocchi +3
Large-scale vision-and-language models, such as CLIP, are typically trained on web-scale data, which can introduce inappropriate content and lead to the development of unsafe and b…
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…
With a Little Help from your own Past: Prototypical Memory Networks for Image Captioning
Manuele Barraco, Sara Sarto, Marcella Cornia +2
Image captioning, like many tasks involving vision and language, currently relies on Transformer-based architectures for extracting the semantics in an image and translating it int…
Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training
Lorenzo Baraldi, Roberto Amoroso, Marcella Cornia +2
The use of self-supervised pre-training has emerged as a promising approach to enhance the performance of many different visual tasks. In this context, recent approaches have emplo…
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…
Multi-Class Unlearning for Image Classification via Weight Filtering
Samuele Poppi, Sara Sarto, Marcella Cornia +2
Machine Unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network. Unlike existing methods that target a limited subset or a sing…
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Roberto Amoroso, Davide Morelli, Marcella Cornia +3
Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits acros…
Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation
Sara Sarto, Manuele Barraco, Marcella Cornia +2
The CLIP model has been recently proven to be very effective for a variety of cross-modal tasks, including the evaluation of captions generated from vision-and-language architectur…
Embodied Agents for Efficient Exploration and Smart Scene Description
Roberto Bigazzi, Marcella Cornia, Silvia Cascianelli +2
The development of embodied agents that can communicate with humans in natural language has gained increasing interest over the last years, as it facilitates the diffusion of robot…