Visual representation learning using graph-based higher-order heuristic distillation for cell detection in blood smear images

Visual representation learning using graph-based higher-order heuristic distillation for cell detection in blood smear images

Blog Article

Background and objective: In many real-world scenarios, including the blood smear domain, it is difficult for detection networks to achieve good performance because image annotation is usually time consuming and expensive.To address this issue, similarity-based distillation (SD) methods, considered the soft version of contrastive learning, are applied to learn a better visual representation without requiring any supervision of the downstream task.Motivated by our theoretical analysis, we treat standard SD methods as the maximization of common orange zinger tomato 1-hop neighboring key points between two queries in an attributed graph, where nodes represent query and key data points.However, such first-order graph heuristic methods that calculate the likelihood of an unseen link between target nodes by using up to 1-hop neighborhoods are normally limited by insufficient representation power and even lack of generalization ability.

Methods: Therefore, in this paper, we propose a novel higher-order heuristic distillation (H2D) method that distills knowledge about more general and powerful higher-order heuristic features based on a more than 1-hop relationship in the attributed graph.To do this, we utilize a graph neural network model to learn the higher-order heuristic features on the attributed graph constructed by query and key color block iphone case data representations and transfer the knowledge from the teacher to the student encoder.Results: Our method outperforms the previous state-of-the-art SD methods in the cell detection task on the blood smear dataset as well on open databases (Pascal VOC and MS COCO).Conclusions: Our proposed model allow teacher encoder to transfer the knowledge about more general and powerful higher-order heuristic embeddings to the student and enables better learning for visual representation on cell detection task using blood smear images.