activity
20162024
most citedNP-Match: When Neural Processes meet Semi-Supervised Learning

6 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Box2Flow: Instance-based Action Flow Graphs from Videos

Jiatong Li, Kalliopi Basioti, Vladimir Pavlovic

A large amount of procedural videos on the web show how to complete various tasks. These tasks can often be accomplished in different ways and step orderings, with some steps able…

cs.CV2023

ALWOD: Active Learning for Weakly-Supervised Object Detection

Yuting Wang, Velibor Ilic, Jiatong Li +2

Object detection (OD), a crucial vision task, remains challenged by the lack of large training datasets with precise object localization labels. In this work, we propose ALWOD, a n…

cs.CV20232 cited

NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation

Jianfeng Wang, Daniela Massiceti, Xiaolin Hu +2

Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. This is useful in a wide range of real-world applications where col…

cs.LG20226 cited

NP-Match: When Neural Processes meet Semi-Supervised Learning

Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti +3

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this wor…

cs.DB20164 cited

Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment

Shahriar Shariat, Vladimir Pavlovic

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings match…