4 citations · 12 across the 7 of their papers we have counts for
12 papers · 1 filter
DIODE: Dilatable Incremental Object Detection
Can Peng, Kun Zhao, Sam Maksoud +2
To accommodate rapid changes in the real world, the cognition system of humans is capable of continually learning concepts. On the contrary, conventional deep learning models lack…
Polynomial Trajectory Predictions for Improved Learning Performance
Ido Freeman, Kun Zhao, Anton Kummert
The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short to mid-term trajectory prediction. Anticipating the unfolding path of…
SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation
Can Peng, Kun Zhao, Sam Maksoud +2
Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will…
PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information
Thomas Kurbiel, Akash Sachdeva, Kun Zhao +1
The ability to predict multiple possible future positions of the ego-vehicle given the surrounding context while also estimating their probabilities is key to safe autonomous drivi…
SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification
Sam Maksoud, Kun Zhao, Peter Hobson +2
The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems. Since mo…
Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN
Can Peng, Kun Zhao, Brian C. Lovell
The human vision and perception system is inherently incremental where new knowledge is continually learned over time whilst existing knowledge is retained. On the other hand, deep…