activity
20152024
most citedLearning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

103 citations · 364 across the 43 of their papers we have counts for

collaborators
Showing 2023 · cs.CVShow all

14 papers · 2 filters

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…

cs.CV2023★ 3 cited

Object-aware Gaze Target Detection

Francesco Tonini, Nicola Dall'Asen, Cigdem Beyan +1

Gaze target detection aims to predict the image location where the person is looking and the probability that a gaze is out of the scene. Several works have tackled this task by re…

cs.CV2023

Semi-supervised learning made simple with self-supervised clustering

Enrico Fini, Pietro Astolfi, Karteek Alahari +4

Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partiall…