323 citations · 1.4k across the 44 of their papers we have counts for
39 papers · 1 filter
Few-Shot Class-Incremental Learning from an Open-Set Perspective
Can Peng, Kun Zhao, Tianren Wang +2
The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments. The challenge of new task lea…
FaceCook: Face Generation Based on Linear Scaling Factors
Tianren Wang, Can Peng, Teng Zhang +1
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face imag…
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…
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…
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement
Tianren Wang, Teng Zhang, Brian Lovell
Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis tasks, the textual descr…
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…