7 citations · 18 across the 6 of their papers we have counts for
4 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…
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…