1 code implementation • ECCV 2020 • My Kieu, Andrew D. Bagdanov, Marco Bertini, Alberto del Bimbo
Despite its broad application and interest, it remains a challenging problem in part due to the vast range of conditions under which it must be robust.
2 code implementations • 5 May 2024 • Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, Alberto del Bimbo
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 incorporating the changes specified in the relative caption.
2 code implementations • 21 Mar 2024 • Alberto Baldrati, Davide Morelli, Marcella Cornia, Marco Bertini, Rita Cucchiara
Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body.
1 code implementation • 17 Mar 2024 • Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini
In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate representations that correlate with the inherent quality of the images.
Blind Image Quality Assessment No-Reference Image Quality Assessment +1
1 code implementation • 7 Nov 2023 • Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, Alberto del Bimbo
Given that, in this context, the speaker is typically in front of the camera and remains the same for the entire duration of the transmission, we can maintain a set of reference keyframes of the person from the higher-quality I-frames that are transmitted within the video stream and exploit them to guide the visual quality improvement; a novel aspect of this approach is the update policy that maintains and updates a compact and effective set of reference keyframes.
1 code implementation • 7 Nov 2023 • Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, Alberto del Bimbo
In this paper, we present a system to restore analog videos of historical archives.
Ranked #2 on Analog Video Restoration on TAPE
1 code implementation • 20 Oct 2023 • Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, Alberto del Bimbo
In this work, we propose a self-supervised approach named ARNIQA (leArning distoRtion maNifold for Image Quality Assessment) for modeling the image distortion manifold to obtain quality representations in an intrinsic manner.
Ranked #2 on No-Reference Image Quality Assessment on CSIQ
Blind Image Quality Assessment No-Reference Image Quality Assessment +1
2 code implementations • 20 Oct 2023 • Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, Alberto del Bimbo
We design a transformer-based Swin-UNet network that exploits both neighboring and reference frames via our Multi-Reference Spatial Feature Fusion (MRSFF) blocks.
Ranked #1 on Analog Video Restoration on TAPE
1 code implementation • 12 Oct 2023 • Giovanni Burbi, Alberto Baldrati, Lorenzo Agnolucci, Marco Bertini, Alberto del Bimbo
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.
Ranked #1 on Hateful Meme Classification on HarMeme
no code implementations • 21 Sep 2023 • Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
Given the recent advances in multimodal image pretraining where visual models trained with semantically dense textual supervision tend to have better generalization capabilities than those trained using categorical attributes or through unsupervised techniques, in this work we investigate how recent CLIP model can be applied in several tasks in artwork domain.
1 code implementation • 11 Sep 2023 • Giuseppe Cartella, Alberto Baldrati, Davide Morelli, Marcella Cornia, Marco Bertini, Rita Cucchiara
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements.
1 code implementation • 22 Aug 2023 • Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
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 the modifications expressed by the caption.
Ranked #6 on Image Retrieval on CIRR
no code implementations • 26 Jul 2023 • Lorenzo Agnolucci, Alberto Baldrati, Francesco Todino, Federico Becattini, Marco Bertini, Alberto del Bimbo
Among these, the CLIP model has shown remarkable capabilities for zero-shot transfer by matching an image and a custom textual prompt in its latent space.
no code implementations • 1 Jun 2023 • Lorenzo Berlincioni, Stefano Berretti, Marco Bertini, Alberto del Bimbo
Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e. g., LiDAR in autonomous or assisted driving).
1 code implementation • 22 May 2023 • Davide Morelli, Alberto Baldrati, Giuseppe Cartella, Marcella Cornia, Marco Bertini, Rita Cucchiara
In this context, image-based virtual try-on, which consists in generating a novel image of a target model wearing a given in-shop garment, has yet to capitalize on the potential of these powerful generative solutions.
1 code implementation • ICCV 2023 • Alberto Baldrati, Davide Morelli, Giuseppe Cartella, Marcella Cornia, Marco Bertini, Rita Cucchiara
Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner.
no code implementations • 27 Mar 2023 • Emanuele Vivoli, Luca Bossi, Marco Bertini, Pierluigi Falorni, Lorenzo Capineri
Holographic imaging is a technique that uses microwave energy to create a three-dimensional image of an object or scene.
2 code implementations • ICCV 2023 • Alberto Baldrati, Lorenzo Agnolucci, Marco Bertini, Alberto del Bimbo
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 images.
2 code implementations • CVPRW 2022 • Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder.
Ranked #3 on Image Retrieval on LaSCo
Composed Image Retrieval (CoIR) Content-Based Image Retrieval +2
2 code implementations • CVPR 2022 • Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
the visual content of the query image.
Ranked #9 on Image Retrieval on CIRR
no code implementations • 25 Jun 2021 • Francesco Bongini, Lorenzo Berlincioni, Marco Bertini, Alberto del Bimbo
In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, compositing synthetic 3D objects within real scenes.
no code implementations • 3 Feb 2021 • My Kieu, Lorenzo Berlincioni, Leonardo Galteri, Marco Bertini, Andrew D. Bagdanov, Alberto del Bimbo
Experimental results demonstrate the effectiveness of our approach: using less than 50\% of available real thermal training data, and relying on synthesized data generated by our model in the domain adaptation phase, our detector achieves state-of-the-art results on the KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal data is available adding GAN generated images to the training data results in improved performance, thus showing that these images act as an effective form of data augmentation.
no code implementations • 27 Aug 2020 • Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto del Bimbo
As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images.
no code implementations • 21 Apr 2020 • Federico Vaccaro, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network.
no code implementations • ICCV 2017 • Leonardo Galteri, Lorenzo Seidenari, Marco Bertini, Alberto del Bimbo
Moreover we show that our approach can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.
no code implementations • 10 May 2016 • Simone Ercoli, Marco Bertini, Alberto del Bimbo
In this paper we present an efficient method for visual descriptors retrieval based on compact hash codes computed using a multiple k-means assignment.
1 code implementation • 28 Mar 2015 • Xirong Li, Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G. M. Snoek, Alberto del Bimbo
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.
no code implementations • 2 Jul 2014 • Lamberto Ballan, Marco Bertini, Giuseppe Serra, Alberto del Bimbo
Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing.