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Explicit Margin Equilibrium for Few-Shot Object Detection

delete2024-01-01
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PRE
AI
刘畅 cover
刘畅 (Chang Liu)
L
Li, BH
M
Mengnan Shi
X
Xiaozhong Chen
Q
Qixiang Ye
季向阳 (Xiangyang Ji) *
DOI:10.1109/TNNLS.2024.3422216delete
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Abstract

Abstract

En 中文
Under low data regimes, few-shot object detection (FSOD) transfers related knowledge from base classes with sufficient annotations to novel classes with limited samples in a two-step paradigm, including base training and balanced fine-tuning. In base training, the learned embedding space needs to be dispersed with large class margins to facilitate novel class accommodation and avoid feature aliasing while in balanced fine-tuning properly concentrating with small margins to represent novel classes precisely. Although obsession with the discrimination and representation dilemma has stimulated substantial progress, explorations for the equilibrium of class margins within the embedding space are still in full swing. In this study, we propose a class margin optimization scheme, termed explicit margin equilibrium (EME), by explicitly leveraging the quantified relationship between base and novel classes. EME first maximizes base-class margins to reserve adequate space to prepare for novel class adaptation. During fine-tuning, it quantifies the interclass semantic relationships by calculating the equilibrium coefficients based on the assumption that novel instances can be represented by linear combinations of base-class prototypes. EME finally reweights margin loss using equilibrium coefficients to adapt base knowledge for novel instance learning with the help of instance disturbance (ID) augmentation. As a plug-and-play module, EME can also be applied to few-shot classification. Consistent performance gains upon various baseline methods and benchmarks validate the generality and efficacy of EME. The code is available at github.com/Bohao-Lee/EME.
Keywords:
Training
Object detection
Detectors
Head
Feature extraction
Semantics
Optimization
Class equilibrium coefficients
few-shot learning (FSL)
few-shot object detection (FSOD)
instance disturbance (ID)
linear representation
margin equilibrium

Journal

IEEE Transactions on Neural Networks and Learning Systems cover
IEEE Transactions on Neural Networks and Learning Systems
IF:
8.9
Papers:
7.5K
Citations:
7.2W

Organization

T
tsinghua university
Scholars:
11.7W
Papers: 10.0W
Citations: 137
C
chinese academy of sciences
Scholars:
56.0W
Papers: 44.7W
Citations: 704