A Dual Weighting Label Assignment Scheme for Object Detection (2203.09730v1)

Abstract: Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight. Such a mechanism limits the learning capacity of detectors. In this paper, we explore a new weighting paradigm, termed dual weighting (DW), to specify pos and neg weights separately. We first identify the key influential factors of pos/neg weights by analyzing the evaluation metrics in object detection, and then design the pos and neg weighting functions based on them. Specifically, the pos weight of a sample is determined by the consistency degree between its classification and localization scores, while the neg weight is decomposed into two terms: the probability that it is a neg sample and its importance conditioned on being a neg sample. Such a weighting strategy offers greater flexibility to distinguish between important and less important samples, resulting in a more effective object detector. Equipped with the proposed DW method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1x schedule, outperforming other existing LA methods. It consistently improves the baselines on COCO by a large margin under various backbones without bells and whistles. Code is available at https://github.com/strongwolf/DW.

• The paper introduces a dual weighting scheme that decouples positive and negative training sample weights to enhance object detection performance.
• It formulates weighting functions based on classification-localization consistency and sample significance, yielding a 41.5% mAP improvement on MS COCO.
• The method demonstrates robust enhancements across varied detector backbones and object scales, paving the way for adaptive, future object detection architectures.

An Analytical Overview of "A Dual Weighting Label Assignment Scheme for Object Detection"

The paper "A Dual Weighting Label Assignment Scheme for Object Detection" by Shuai Li et al. proposes an innovative approach to the label assignment (LA) challenge in object detection, integrating a Dual Weighting (DW) scheme to independently assign positive (pos) and negative (neg) weights to training samples. This paper targets the limitations inherent in existing LA methodologies which predominantly focus on positive weight determination, with negative weights directly derived from their positive counterparts.

Dual Weighting Approach and Technical Contributions

The DW method introduces significant flexibility by unshackling the dependence of neg weights on pos weights, allowing for a more nuanced supervision mechanism. Key factors influencing pos/neg weights are first identified, and weighting functions are formulated accordingly. The pos weight is contingent upon the consistency between a sample’s classification and localization scores, while the neg weight is decomposed into a neg sample probability and its significance given it is a neg sample.

Formulation and Evaluation:

The pos and neg weights are strategically specified to align with the evaluation metrics employed in object detection. By leveraging indicators such as classification scores and IoU, DW provides a comprehensive framework that enhances the delineation between critical and non-critical samples. The paper quantifies performance benefits through the mean Average Precision (mAP) metric on MS COCO, exhibiting a 41.5% mAP for a single FCOS-ResNet-50 detector, marking a substantial improvement relative to existing methods.

Comprehensive Experimental Benchmarking

The empirical analysis presented in the paper is meticulously detailed. DW was tested against baseline methodologies, demonstrating consistent improvements. It showcased versatility across various backbone configurations, achieving enhanced AP across small, medium, and large objects when compared to hard and soft LA techniques. The integration of a lightweight box refinement module further amplified the weighting function's efficiency by refining positional accuracies, thereby bolstering detection performance.

Theoretical and Practical Implications

The introduction of a decoupled weighting strategy propels a theoretical shift in how label assignments are approached, suggesting that uncoupled positive and negative weights can significantly elevate object detection efficacy. Practically, DW fosters robust learning of dense object detectors by distinguishing pivotal from trivial training samples, mitigating the inconsistencies across classification and regression tasks.

Future Prospects:

This paper opens pathways for further investigation into adaptive weighting schemas and their potential integration into more sophisticated network architectures or emergent AI frameworks. The modular nature of DW posits it as an attractive strategy in evolving object detection paradigms, particularly in optimizations involving complex scenes and dynamic conditions.

Despite its promising advances, the paper duly acknowledges potential societal ramifications — particularly concerning privacy and ethical deployment in sensitive domains such as surveillance and military applications. These considerations underscore the need for responsible dissemination and utilization of such technological innovations.