29 citations · 181 across the 51 of their papers we have counts for
8 papers · 1 filter
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…
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
Gang Qiao, Kaidong Hu, Seonghyeon Moon +4
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection a…
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…
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…
SAViR-T: Spatially Attentive Visual Reasoning with Transformers
Pritish Sahu, Kalliopi Basioti, Vladimir Pavlovic
We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM). Our model considers explicit spat…