29 citations · 181 across the 51 of their papers we have counts for
32 papers · 1 filter
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…
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…
M3Act: Learning from Synthetic Human Group Activities
Che-Jui Chang, Danrui Li, Deep Patel +7
The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the…
D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation
Yuting Wang, Ricardo Guerrero, Vladimir Pavlovic
Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes le…
MSI: Maximize Support-Set Information for Few-Shot Segmentation
Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou +4
FSS(Few-shot segmentation) aims to segment a target class using a small number of labeled images(support set). To extract information relevant to the target class, a dominant appro…
Visual Semantic Parsing: From Images to Abstract Meaning Representation
Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu +4
The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e.g., image) into a structured representation, where…