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20232025
most citedZero-Shot Composed Image Retrieval with Textual Inversion

3 citations · 9 across the 12 of their papers we have counts for

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cs.CV2025

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…

cs.CV2025

Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and Resolution

Vlad Hosu, Lorenzo Agnolucci, Daisuke Iso +1

Image Quality Assessment (IQA) measures and predicts perceived image quality by human observers. Although recent studies have highlighted the critical influence that variations in…

cs.CV2024

AIM 2024 Challenge on UHD Blind Photo Quality Assessment

Vlad Hosu, Marcos V. Conde, Lorenzo Agnolucci +3

We introduce the AIM 2024 UHD-IQA Challenge, a competition to advance the No-Reference Image Quality Assessment (NR-IQA) task for modern, high-resolution photos. The challenge is b…

cs.CV2024

UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment

Vlad Hosu, Lorenzo Agnolucci, Oliver Wiedemann +2

We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA…

cs.CV2024★ 2 cited

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…

cs.CV2024★ 1 cited

Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment

Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini

No-Reference Image Quality Assessment (NR-IQA) focuses on designing methods to measure image quality in alignment with human perception when a high-quality reference image is unava…

cs.CV2023

Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN

Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1

In the latest years, videoconferencing has taken a fundamental role in interpersonal relations, both for personal and business purposes. Lossy video compression algorithms are the…

cs.CV2023

Restoration of Analog Videos Using Swin-UNet

Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1

In this paper, we present a system to restore analog videos of historical archives. These videos often contain severe visual degradation due to the deterioration of their tape supp…

cs.CV2023★ 3 cited

ARNIQA: Learning Distortion Manifold for Image Quality Assessment

Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1

No-Reference Image Quality Assessment (NR-IQA) aims to develop methods to measure image quality in alignment with human perception without the need for a high-quality reference ima…

cs.CV2023

Reference-based Restoration of Digitized Analog Videotapes

Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1

Analog magnetic tapes have been the main video data storage device for several decades. Videos stored on analog videotapes exhibit unique degradation patterns caused by tape aging…

cs.CV2023

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.…

cs.CV2023

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…

cs.CV2023★ 3 cited

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…