activity
20112026
most citedGeneralizing to Unseen Domains via Adversarial Data Augmentation

361 citations · 944 across the 48 of their papers we have counts for

collaborators

58 papers

cs.CV2026

TokenMatch: 3D Mesh Correspondence Transformer with Curvature-Guided Tokenisation

Adeela Islam, Zorah Lähner, Vittorio Murino +1

While data-driven 3D shape correspondence estimation has recently seen substantial progress, robust matching under partial observations and strong non-isometric deformations remain…

cs.CV2023

Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videos

Sanket Thakur, Cigdem Beyan, Pietro Morerio +2

Objects are crucial for understanding human-object interactions. By identifying the relevant objects, one can also predict potential future interactions or actions that may occur w…

cs.CV2023

Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention

Sanket Thakur, Cigdem Beyan, Pietro Morerio +2

Short-term action anticipation (STA) in first-person videos is a challenging task that involves understanding the next active object interactions and predicting future actions. Exi…

cs.CV2023★ 1 cited

Guided Attention for Next Active Object @ EGO4D STA Challenge

Sanket Thakur, Cigdem Beyan, Pietro Morerio +2

In this technical report, we describe the Guided-Attention mechanism based solution for the short-term anticipation (STA) challenge for the EGO4D challenge. It combines the object…

cs.CV2023

Target-driven One-Shot Unsupervised Domain Adaptation

Julio Ivan Davila Carrazco, Suvarna Kishorkumar Kadam, Pietro Morerio +2

In this paper, we introduce a novel framework for the challenging problem of One-Shot Unsupervised Domain Adaptation (OSUDA), which aims to adapt to a target domain with only a sin…

cs.CV2023

Continual Source-Free Unsupervised Domain Adaptation

Waqar Ahmed, Pietro Morerio, Vittorio Murino

Existing Source-free Unsupervised Domain Adaptation (SUDA) approaches inherently exhibit catastrophic forgetting. Typically, models trained on a labeled source domain and adapted t…