1 citations · 2 across the 5 of their papers we have counts for
5 papers
SABER-6D: Shape Representation Based Implicit Object Pose Estimation
Shishir Reddy Vutukur, Mengkejiergeli Ba, Benjamin Busam +2
In this paper, we propose a novel encoder-decoder architecture, named SABER, to learn the 6D pose of the object in the embedding space by learning shape representation at a given p…
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Karim Guirguis, Johannes Meier, George Eskandar +3
Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neura…
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
Karim Guirguis, Mohamed Abdelsamad, George Eskandar +4
Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learn…
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
Karim Guirguis, Ahmed Hendawy, George Eskandar +3
Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-…
Few-Shot Object Detection in Unseen Domains
Karim Guirguis, George Eskandar, Matthias Kayser +2
Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approac…