Core Concepts
The core message of this paper is to enhance the structured understanding of pre-trained deep neural networks (DNNs) by investigating the hierarchical organization of visual scenes through a novel Visual Hierarchy Mapper (Hi-Mapper).
Abstract
The paper proposes a Visual Hierarchy Mapper (Hi-Mapper) that aims to improve the structured understanding of pre-trained deep neural networks (DNNs) by identifying the hierarchical organization of visual scenes.
The key components of Hi-Mapper are:
Probabilistic modeling of hierarchy nodes: The hierarchy tree is defined by modeling each node as a Gaussian distribution, where the mean vector represents the center of the visual-semantic cluster and the covariance captures the scale of the cluster. Higher-level nodes are modeled as a Mixture of Gaussians (MoG) of their corresponding child nodes.
Mapping hierarchy to hyperbolic space: Since the flat geometry of Euclidean space is suboptimal for representing the exponential growth of hierarchy nodes, Hi-Mapper maps the identified visual hierarchy to hyperbolic space, where the constant negative curvature can effectively capture the hierarchical relations.
Hierarchical contrastive loss: Hi-Mapper optimizes the hierarchical relations in hyperbolic space using a novel hierarchical contrastive loss, which encourages child-parent nodes to be similar and child-child nodes to be dissimilar.
Hierarchy decomposition and encoding: The pre-defined hierarchy tree interacts with the penultimate visual feature map of the pre-trained DNNs to decompose the features into the visual hierarchy. The identified visual hierarchy is then encoded back into the global visual representation to enhance the recognition of the entire scene.
The extensive experiments on image classification, object detection, instance segmentation, and semantic segmentation demonstrate that Hi-Mapper consistently improves the performance of various pre-trained DNNs, including both CNN-based and ViT-based models.
Stats
The paper does not provide any specific numerical data or statistics in the main text. The results are presented in the form of performance comparisons on various benchmarks.
Quotes
The paper does not contain any striking quotes that support the key logics.