Search Results for author:
Found 6 papers, 2 papers with code
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions.
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
Neural Task Planning with And-Or Graph Representations
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 vision research.
Knowledge-Embedded Routing Network for Scene Graph Generation
More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions.
Ranked #9 on
Scene Graph Generation
on Visual Genome
Knowledge Graph Transfer Network for Few-Shot Recognition
In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN).
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition
The framework exploits prior knowledge to guide adaptive information propagation among different categories to facilitate multi-label analysis and reduce the dependency of training samples.
Few-Shot Learning
Multi-label Image Recognition with Partial Labels
Progressive Representative Labeling for Deep Semi-Supervised Learning
In this paper, we propose to label only the most representative samples to expand the labeled set.
