Balanced Classification: A Unified Framework for Long-Tailed Object Detection (2308.02213v1)

Abstract: Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. In this paper, we contend that the learning bias originates from two factors: 1) the unequal competition arising from the imbalanced distribution of foreground categories, and 2) the lack of sample diversity in tail categories. To tackle these issues, we introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution and dynamic intensification of sample diversities in a synchronized manner. Specifically, a novel foreground classification balance loss (FCBL) is developed to ameliorate the domination of head categories and shift attention to difficult-to-differentiate categories by introducing pairwise class-aware margins and auto-adjusted weight terms, respectively. This loss prevents the over-suppression of tail categories in the context of unequal competition. Moreover, we propose a dynamic feature hallucination module (FHM), which enhances the representation of tail categories in the feature space by synthesizing hallucinated samples to introduce additional data variances. In this divide-and-conquer approach, BACL sets a new state-of-the-art on the challenging LVIS benchmark with a decoupled training pipeline, surpassing vanilla Faster R-CNN with ResNet-50-FPN by 5.8% AP and 16.1% AP for overall and tail categories. Extensive experiments demonstrate that BACL consistently achieves performance improvements across various datasets with different backbones and architectures. Code and models are available at https://github.com/Tianhao-Qi/BACL.

• The paper presents BACL, a unified framework combining Foreground Classification Balance Loss and Dynamic Feature Hallucination to mitigate long-tailed data bias.
• It employs pairwise class-aware margins and automatic weight adjustments to enhance classifier focus on underrepresented tail categories.
• Experiments on the LVIS benchmark demonstrate up to a 16.1% AP gain for tail categories and a solid 5.8% overall improvement with a ResNet-50-FPN backbone.

Balanced Classification: A Unified Framework for Long-Tailed Object Detection

The paper "Balanced Classification: A Unified Framework for Long-Tailed Object Detection" introduces the Balanced Classification (BACL) framework as a novel solution to the challenges posed by imbalanced datasets in object detection. In scenarios involving long-tailed data distributions, object detection models often suffer from biased learning, favoring abundant head categories over rare tail categories. This paper aims to address this issue with a structured approach divided into two key components: Foreground Classification Balance Loss (FCBL) and a Dynamic Feature Hallucination Module (FHM).

Key Contributions and Methodology

The proposed BACL framework primarily tackles two challenges in long-tailed object detection: the unequal competition among categories arising from imbalanced data and the paucity of diverse samples in tail categories.

1. Foreground Classification Balance Loss (FCBL): The FCBL introduces pairwise class-aware margins and automatic weight adjustments to rectify the classification bias. This approach aims to balance the suppression gradients during training, allowing the model to adjust its focus towards difficult-to-differentiate categories and reducing the dominance of head categories over tail categories.
2. Dynamic Feature Hallucination Module (FHM): The FHM approaches the problem of limited sample diversity by synthesizing additional features that mimic the variability of tail category samples. This is done using a reparametrization technique that generates new data points from estimated feature distributions, thus enriching the training set with novel examples that aid in model generalization.

These components are implemented within a decoupled training strategy, distinguishing representation learning from classifier learning. By freezing the feature extractor during the classifier learning phase, BACL focuses on adjusting the classifier without compromising the learned representations, which improves performance across different datasets, architectures, and backbones.

Numerical Results and Implications

BACL demonstrates significant improvements on the LVIS (Large Vocabulary Instance Segmentation) benchmark. When integrated with a ResNet-50-FPN backbone, BACL achieved a substantial gain of 5.8% AP overall, with remarkable improvements of 16.1% AP for tail categories over conventional Faster R-CNN models. These results underline the efficacy of BACL in equalizing performance across head and tail categories and highlight the effectiveness of its components in mitigating learning biases.

The strong performance of BACL suggests several implications for future research. Practically, it provides a robust framework that can be applied to other imbalanced classification tasks. Theoretically, it encourages a focus on designing methodologies that incorporate dynamic feature augmentation and adaptive loss functions to further address data imbalance.

BACL's ability to generalize well across various datasets indicates its potential for widespread adoption in real-world applications where data distributions are inherently unbalanced. Future developments of AI systems could further enhance this approach by integrating more sophisticated methods for feature diversification or by extending the framework to additional recognition tasks.

In conclusion, BACL represents a significant step forward in addressing the challenges of long-tailed object detection. Its emphasis on balancing classification processes and enriching sample diversity offers valuable insights into tackling similar issues in other domains of machine learning.