AutoAssign: Differentiable Label Assignment for Dense Object Detection (2007.03496v3)

Abstract: Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledge and achieves appearance-aware through a fully differentiable weighting mechanism. During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions. To adapt to object appearances, Confidence Weighting is proposed to adjust the specific assign strategy of each instance. The two weighting modules are then combined to generate positive and negative weights to adjust each location's confidence. Extensive experiments on the MS COCO show that our method steadily surpasses other best sampling strategies by large margins with various backbones. Moreover, our best model achieves 52.1% AP, outperforming all existing one-stage detectors. Besides, experiments on other datasets, e.g., PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign.

• The paper introduces AutoAssign, a fully differentiable mechanism for dynamic label assignment that reduces reliance on static heuristics.
• It employs center and confidence weighting modules to tailor label assignment based on object appearance and category-specific patterns.
• Empirical results show a benchmark AP of 52.1% on MS COCO, demonstrating enhanced detection performance and broad dataset applicability.

Overview of "AutoAssign: Differentiable Label Assignment for Dense Object Detection"

The paper "AutoAssign: Differentiable Label Assignment for Dense Object Detection" presents an innovative approach within the domain of computer vision, specifically for object detection tasks. The authors propose a method named AutoAssign, which is a novel anchor-free object detector that introduces a fully differentiable and appearance-aware mechanism for label assignment.

The core objective of this research is to enhance the label assignment process in dense object detection, where determining positive and negative samples is a critical challenge. The conventional methods often rely heavily on pre-defined strategies and human expertise, such as using fixed anchor settings or heuristics, which can limit their adaptability and performance. These methods include anchor-based models like RetinaNet and anchor-free models such as FCOS, which utilize static center priors for label assignment.

Main Contributions

1. Differentiable Weighting Mechanism: The primary contribution is the formulation of a differentiable weighting mechanism that facilitates dynamic, data-driven label assignment. This mechanism comprises two vital components: Center Weighting and Confidence Weighting. By integrating these modules, AutoAssign adapts both spatial and scale assignments tailored to object appearances and category characteristics, thereby optimizing label assignment more effectively than static approaches.
2. Center Weighting Module: This module capitalizes on the inherent center prior evident in object detection datasets, but extends it by learning a category-specific distribution. It dynamically adjusts the prior distribution to meet the distinct shape patterns of different categories using a modified Gaussian-shape function. This adaptation allows the model to account for variations in object appearance across categories, thereby improving the selection of positive sampling locations.
3. Confidence Weighting Module: The Confidence Weighting module assesses both the classification and regression likelihood to determine the aptitude of a sample being a positive instance. This module contributes to filter out background noise by effectively weighting positive and negative samples, which enhances the robustness of object localization. It incorporates an Implicit-Objectness score for each prediction to improve discrimination between foreground and background.

Empirical Evaluation

Extensive experiments on standard datasets, notably MS COCO, show that AutoAssign surpasses existing dense sampling strategies by significant margins across various backbones. The proposed model achieves an Average Precision (AP) of 52.1\% on MS COCO, which marks a new benchmark for one-stage object detectors at the time of writing. Additionally, the model's efficacy is verified across other diversified datasets such as PASCAL VOC, Objects365, and WiderFace, demonstrating its broad applicability and robust generalization capabilities without necessitating bespoke adaptations for different tasks.

Implications and Future Directions

The results substantiate the claim that dynamically learning the label assignment process can substantially enhance detection performance. By reducing reliance on manual heuristics and static configurations, AutoAssign paves the way for more adaptive and efficient object detection frameworks. This advancement invites further exploration in simplifying the weighting mechanism, potentially leading to more computationally efficient models.

Future research directions could involve integrating more sophisticated learnable parameters within the weighting modules or extending this approach to more complex detection tasks, such as those involving 3D objects or videos. The adaptability and transferability of AutoAssign suggest promising applications in real-world settings where diverse object scales and types present substantial challenges.

In conclusion, the AutoAssign approach represents a significant advancement in the field of dense object detection, improving the flexibility and performance of label assignment processes. Its fully differentiable design makes it a relevant model for future innovations in the area of computer vision.