activity
20152023
most citedLearning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

103 citations · 309 across the 21 of their papers we have counts for

collaborators
Showing cs.CVShow all

48 papers · 1 filter

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…

cs.CV20223 cited

ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing

Giulio Mattolin, Luca Zanella, Elisa Ricci +1

Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are no…

cs.CV20221 cited

Overlap-guided Gaussian Mixture Models for Point Cloud Registration

Guofeng Mei, Fabio Poiesi, Cristiano Saltori +3

Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs i…

cs.CV20223 cited

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…

cs.CV20221 cited

Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation

Yangsong Zhang, Subhankar Roy, Hongtao Lu +2

In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unann…

cs.CV20221 cited

Playable Environments: Video Manipulation in Space and Time

Willi Menapace, Stéphane Lathuilière, Aliaksandr Siarohin +4

We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framew…