22 citations · 81 across the 11 of their papers we have counts for
11 papers
Dual Path Structural Contrastive Embeddings for Learning Novel Objects
Bingbin Li, Elvis Han Cui, Yanan Li +2
Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas. Recent research on either meta-learning based or transfer-learn…
Class-Incremental Few-Shot Object Detection
Pengyang Li, Yanan Li, Han Cui +1
Conventional detection networks usually need abundant labeled training samples, while humans can learn new concepts incrementally with just a few examples. This paper focuses on a…
Fine-grained Semantic Constraint in Image Synthesis
Pengyang Li, Donghui Wang
In this paper, we propose a multi-stage and high-resolution model for image synthesis that uses fine-grained attributes and masks as input. With a fine-grained attribute, the propo…
MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination
Shengyu Zhang, Donghui Wang, Zhou Zhao +3
In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance. Existing methods for text-t…
Learning Robust Features with Incremental Auto-Encoders
Yanan Li, Donghui Wang
Automatically learning features, especially robust features, has attracted much attention in the machine learning community. In this paper, we propose a new method to learn non-lin…
Zero-Shot Learning with Generative Latent Prototype Model
Yanan Li, Donghui Wang
Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most e…