Rank & Sort Loss for Object Detection and Instance Segmentation (2107.11669v2)

Abstract: We propose Rank & Sort (RS) Loss, a ranking-based loss function to train deep object detection and instance segmentation methods (i.e. visual detectors). RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.) their localisation qualities (e.g. Intersection-over-Union - IoU). To tackle the non-differentiable nature of ranking and sorting, we reformulate the incorporation of error-driven update with backpropagation as Identity Update, which enables us to model our novel sorting error among positives. With RS Loss, we significantly simplify training: (i) Thanks to our sorting objective, the positives are prioritized by the classifier without an additional auxiliary head (e.g. for centerness, IoU, mask-IoU), (ii) due to its ranking-based nature, RS Loss is robust to class imbalance, and thus, no sampling heuristic is required, and (iii) we address the multi-task nature of visual detectors using tuning-free task-balancing coefficients. Using RS Loss, we train seven diverse visual detectors only by tuning the learning rate, and show that it consistently outperforms baselines: e.g. our RS Loss improves (i) Faster R-CNN by ~ 3 box AP and aLRP Loss (ranking-based baseline) by ~ 2 box AP on COCO dataset, (ii) Mask R-CNN with repeat factor sampling (RFS) by 3.5 mask AP (~ 7 AP for rare classes) on LVIS dataset; and also outperforms all counterparts. Code is available at: https://github.com/kemaloksuz/RankSortLoss

• The paper introduces RS Loss, a novel ranking and sorting loss function that eliminates auxiliary heads and sampling heuristics to streamline training.
• The paper leverages an Identity Update mechanism to integrate error-driven updates, enhancing interpretability and effective management of intra-class errors.
• Comprehensive experiments on COCO and LVIS demonstrate that RS Loss improves performance by up to 3 AP, robustly addressing class imbalance in varied architectures.

Rank Sort Loss for Object Detection and Instance Segmentation: A Rigorous Examination

The paper proposes a novel ranking-based loss function named Rank Sort (RS) Loss aimed at improving the performance of object detection and instance segmentation tasks using deep learning models. This methodological advancement optimizes visual detectors by introducing both ranking and sorting components to handle class imbalance robustly and eliminate the need for auxiliary heads and sampling heuristics during training. The discourse is enriched by comprehensive experimentation across multiple architectures and datasets, establishing RS Loss as a significant enhancement in simplifying and improving the training of visual detection systems.

Core Contributions

The focal point of RS Loss is its dual mechanism, which ranks positives above negatives and sorts positives based on their localization quality using Intersection-over-Union (IoU) values. This prioritization, formed through a novel differentiable implementation called Identity Update, is distinct from conventional ranking-based losses such as Average Precision (AP) Loss and average Localisation Recall Precision (aLRP) Loss, which mainly focus on ranking positives above negatives.

1. Identity Update: Extending beyond the error-driven updates typical in ranking-based approaches, the Identity Update reformulates error incorporation in backpropagation, ensuring interpretability of loss values and allowing intra-class errors to enhance model supervision.
2. Simplification and Robustness: RS Loss eliminates the complexities of auxiliary heads and sampling heuristics inherent in many contemporary detectors, providing robustness against varying levels of class imbalance—an attribute underscored by the elimination of hyper-parameter tuning apart from the learning rate.
3. Comprehensive Performance Enhancement: The paper rigorously examines RS Loss across diverse visual detectors—encompassing both multi-stage and single-stage architectures on benchmarks like COCO and LVIS. RS Loss consistently outperformed baselines, enhancing models by up to 3 AP in challenging domains with inherent class imbalances, and showing superior performance even against models employing auxiliary heads.

Implications and Prospects

The implications of RS Loss are significant for both theoretical understanding and practical applications. Theoretically, it provides a novel framework for integrating differentiable sorting into loss functions, expanding the horizon of optimization strategies in non-differentiable settings. Practically, it sets a precedent for simplified training pipelines with fewer hyper-parameters, which could catalyze the deployment of more efficient and scalable detection systems in real-world applications.

Future prospects involve extending RS Loss to other domains where ranking and sorting play pivotal roles, such as metric learning and natural language processing tasks. Furthermore, exploring adaptive learning rate strategies and integrating RS Loss within end-to-end learning systems could yield powerful models without intricate parameter tuning.

In summary, RS Loss not only offers methodological advancements but also pragmatically contributes to the evolution of more robust, interpretable, and scalable detection frameworks, which are indispensable in the advancing field of AI and computer vision.