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Probing the 3D Object-Level Understanding of Pre-Trained Detection Transformers
Robin Kim, Colin Samplawski, Benjamin M. Marlin
Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded into 2D bounding boxes and clas…
FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors
Jason Wu, Ziqi Wang, Xiaomin Ouyang +5
Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sen…
Heteroskedastic Geospatial Tracking with Distributed Camera Networks
Colin Samplawski, Shiwei Fang, Ziqi Wang +3
Visual object tracking has seen significant progress in recent years. However, the vast majority of this work focuses on tracking objects within the image plane of a single camera…
Learning Graph-Based Priors for Generalized Zero-Shot Learning
Colin Samplawski, Jannik Wolff, Tassilo Klein +1
The task of zero-shot learning (ZSL) requires correctly predicting the label of samples from classes which were unseen at training time. This is achieved by leveraging side informa…
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification
Colin Samplawski, Heesung Kwon, Erik Learned-Miller +1
Zero-shot learning (ZSL) is one of the most extreme forms of learning from scarce labeled data. It enables predicting that images belong to classes for which no labeled training in…