42 citations · 74 across the 6 of their papers we have counts for
9 papers
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition
Bin Sun, Dehui Kong, Shaofan Wang +3
Zero-shot action recognition can recognize samples of unseen classes that are unavailable in training by exploring common latent semantic representation in samples. However, most m…
ZooBuilder: 2D and 3D Pose Estimation for Quadrupeds Using Synthetic Data
Abassin Sourou Fangbemi, Yi Fei Lu, Mao Yuan Xu +3
This work introduces a novel strategy for generating synthetic training data for 2D and 3D pose estimation of animals using keyframe animations. With the objective to automate the…
Multi-column Point-CNN for Sketch Segmentation
Fei Wang, Shujin Lin, Hanhui Li +4
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation…
Neural Task Planning with And-Or Graph Representations
Tianshui Chen, Riquan Chen, Lin Nie +3
This paper focuses on semantic task planning, i.e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer…
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Tianshui Chen, Wenxi Wu, Yuefang Gao +3
Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized acc…
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition…