Bucketed Ranking-based Losses for Efficient Training of Object Detectors

🌈 Abstract

The paper focuses on improving the efficiency of ranking-based loss functions, such as Average Precision (AP) Loss and Rank & Sort (RS) Loss, for training object detectors. These loss functions outperform widely used score-based losses, but have high time and space complexities due to the need for pairwise comparisons among positive and negative predictions.

🙋 Q&A

[01] Ranking-based Loss Functions

1. What are the benefits of ranking-based loss functions for object detection?

• They are inherently robust to imbalance between positive and negative classes, and do not require sampling mechanisms.
• They offer significant performance gains over score-based losses and have fewer hyperparameters, making them easier to tune.

2. What is the main drawback of ranking-based loss functions?

• They require pairwise comparisons among positive and negative predictions, leading to a quadratic time complexity that is prohibitive when the number of predictions is large.

[02] Bucketed Ranking-based Losses

1. How does the proposed bucketing approach address the efficiency issue of ranking-based losses?

• The approach groups negative predictions into buckets to reduce the number of pairwise comparisons, thereby reducing the time complexity from quadratic to linear.
• The bucketing approach preserves the same gradients as the original ranking-based losses when the number of buckets is equal to the number of negatives.

2. How does the bucketed ranking-based losses improve the training of transformer-based object detectors?

• The improved efficiency of the bucketed losses enables, for the first time, the training of transformer-based object detectors using ranking-based losses.
• When the bucketed RS Loss is used to train the state-of-the-art transformer-based detector CoDETR, it consistently outperforms the original CoDETR results across different backbones.

[03] Experiments and Results

1. What are the key findings from the experiments on CNN-based detectors?

• The bucketed ranking-based losses yield the same accuracy as the unbucketed versions while providing faster training, up to 2x speedup.
• Compared to score-based losses and other ranking-based losses, the bucketed losses are either superior or on par in terms of accuracy, training time, and tuning simplicity.

2. How do the bucketed ranking-based losses perform on transformer-based detectors?

• The bucketed RS Loss enables significantly more efficient training of the transformer-based CoDETR detector, reducing the training time by 6x compared to the original RS Loss.
• The BRS-DETR, which incorporates the bucketed RS Loss into CoDETR, outperforms the original CoDETR across different backbones.

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