5 citations · 18 across the 16 of their papers we have counts for
17 papers
Zero-Shot Temporal Action Localization Through Textual Guidance
Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3
Zero-shot temporal action localization (ZS-TAL) consists of classifying and localizing actions in untrimmed videos, where action classes are unseen at training time. Existing work…
Towards Unconstrained Human-Object Interaction
Francesco Tonini, Alessandro Conti, Lorenzo Vaquero +2
Human-Object Interaction (HOI) detection is a longstanding computer vision problem concerned with predicting the interaction between humans and objects. Current HOI models rely on…
Specificity-aware reinforcement learning for fine-grained open-world classification
Samuele Angheben, Davide Berasi, Alessandro Conti +2
Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Mu…
Large Multimodal Models as General In-Context Classifiers
Marco Garosi, Matteo Farina, Alessandro Conti +2
Which multimodal model should we use for classification? Previous studies suggest that the answer lies in CLIP-like contrastive Vision-Language Models (VLMs), due to their remarkab…
ConViS-Bench: Estimating Video Similarity Through Semantic Concepts
Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3
What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where t…
Dynamic Scoring with Enhanced Semantics for Training-Free Human-Object Interaction Detection
Francesco Tonini, Lorenzo Vaquero, Alessandro Conti +2
Human-Object Interaction (HOI) detection aims to identify humans and objects within images and interpret their interactions. Existing HOI methods rely heavily on large datasets wit…
Compositional Caching for Training-free Open-vocabulary Attribute Detection
Marco Garosi, Alessandro Conti, Gaowen Liu +2
Attribute detection is crucial for many computer vision tasks, as it enables systems to describe properties such as color, texture, and material. Current approaches often rely on l…
On Large Multimodal Models as Open-World Image Classifiers
Alessandro Conti, Massimiliano Mancini, Enrico Fini +3
Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images…
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models
Anil Osman Tur, Alessandro Conti, Cigdem Beyan +5
In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. The zero-shot assumption is ess…
Automatic benchmarking of large multimodal models via iterative experiment programming
Alessandro Conti, Enrico Fini, Paolo Rota +3
Assessing the capabilities of large multimodal models (LMMs) often requires the creation of ad-hoc evaluations. Currently, building new benchmarks requires tremendous amounts of ma…
Vocabulary-free Image Classification and Semantic Segmentation
Alessandro Conti, Enrico Fini, Massimiliano Mancini +3
Large vision-language models revolutionized image classification and semantic segmentation paradigms. However, they typically assume a pre-defined set of categories, or vocabulary,…
Test-Time Zero-Shot Temporal Action Localization
Benedetta Liberatori, Alessandro Conti, Paolo Rota +2
Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model…
Socially Pertinent Robots in Gerontological Healthcare
Xavier Alameda-Pineda, Angus Addlesee, Daniel Hernández García +41
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation o…
The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation
Giacomo Zara, Alessandro Conti, Subhankar Roy +3
Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target datas…
Vocabulary-free Image Classification
Alessandro Conti, Enrico Fini, Massimiliano Mancini +3
Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categ…
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
Alessandro Conti, Paolo Rota, Yiming Wang +1
Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have dem…
Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss
Riccardo Franceschini, Enrico Fini, Cigdem Beyan +3
Emotion recognition is involved in several real-world applications. With an increase in available modalities, automatic understanding of emotions is being performed more accurately…