activity
20152024
most citedFirst Order Motion Model for Image Animation

145 citations · 984 across the 73 of their papers we have counts for

collaborators

94 papers

cs.RO2024★ 2 cited

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…

cs.CV2024★ 1 cited

Democratizing Fine-grained Visual Recognition with Large Language Models

Mingxuan Liu, Subhankar Roy, Wenjing Li +3

Identifying subordinate-level categories from images is a longstanding task in computer vision and is referred to as fine-grained visual recognition (FGVR). It has tremendous signi…

cs.CV2023

Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation

Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3

Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in vary…

cs.CV2023★ 2 cited

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…

cs.CV2023

On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers

Thomas De Min, Massimiliano Mancini, Karteek Alahari +2

State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forget…

cs.CV2023★ 2 cited

Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations

Anil Osman Tur, Nicola Dall'Asen, Cigdem Beyan +1

This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to lab…