Hyperbolic Embedding Space for Open World Object Detection
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Solution Overview
Problem
Existing object detection systems struggle with open world scenarios where new classes are introduced, leading to distribution shifts and 'catastrophic forgetting', failing to accurately distinguish known and unknown objects due to the lack of clear definitions and contextual understanding.
Innovation Solution
A method utilizing hyperbolic embedding spaces to regularize embeddings of known objects, applying a SuperClass Regularizer and Adaptive Relabeling module to enhance the detection of unknown objects by leveraging hierarchical relationships and semantic similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continual object detection incrementally adds new classes, then the system can handle open world scenarios, but catastrophic forgetting occurs where previously learned knowledge is lost
Solution Approach 1:
The patent applies preliminary action by projecting embeddings into a hyperbolic embedding space before performing detection, where the hierarchical structure of categories is pre-established. This pre-organized structure guides the system in distinguishing known and unknown objects, preventing catastrophic forgetting while enabling continual learning of new classes.
Solution Approach 2:
The patent transitions from traditional Euclidean embedding spaces to hyperbolic embedding spaces, utilizing the additional dimensional characteristics of hyperbolic geometry to represent hierarchical category relationships. This dimensional change enables the system to maintain discriminative power for both known and unknown objects simultaneously.
2Device complexity
If standard object detection assumes a fixed number of classes, then the model is simpler to train, but it fails to accurately distinguish known and unknown objects in open world scenarios
Solution Approach 1:
The patent employs hyperbolic embedding space, which adds a curvature dimension to traditional flat Euclidean spaces. This enables the representation of hierarchical category structures with inherent geometric relationships, allowing the model to distinguish known and unknown objects more accurately while maintaining reasonable training complexity through the use of contrastive loss functions.
Solution Approach 2:
The patent changes the metric parameters of the embedding space from Euclidean to hyperbolic geometry, fundamentally altering how distances and similarities are computed. This parameter change enables better separation of known and unknown object representations while maintaining model trainability through standard optimization techniques adapted to hyperbolic space.
3Measurement precision
If embeddings are projected into hyperbolic space, then hierarchical structure is captured more effectively, but the computational complexity increases
Solution Approach 1:
The patent changes the mathematical parameters of the embedding space to hyperbolic geometry, which naturally captures hierarchical structures through its curvature properties. While this does increase computational complexity compared to Euclidean space, the use of contrastive loss functions and pre-computed hyperbolic averages provides a structured approach to managing this complexity.
Solution Approach 2:
The patent introduces hyperbolic averages as an intermediary concept to simplify the computation of relationships between categories. By pre-computing and storing these averages, the system reduces the computational burden during inference while maintaining accurate hierarchical representation.
Data Source
AI summary
A method of performing open world object detection includes receiving object data, that includes embeddings data corresponding to a plurality of embeddings for known objects in a first input image, projecting the embeddings into a hyperbolic embedding space that includes embeddings in a plurality of categories of objects each including one or more classes of objects, regularizing the projected embeddings within the hyperbolic embedding space by moving each of the projected embeddings closer to embeddings in a same category of the plurality of categories and further away from embeddings in different categories of the plurality of categories, receiving an unmatched query corresponding to an object in a second input image, and generating, based on the hyperbolic embedding space including the regularized embeddings, an output signal that indicates whether the object in the second input image corresponds to an unknown object in one of the classes of objects.