Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection (2003.11818v1)

Abstract: Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or feature fusion layer for object detection. However, these methods focus on searching only one certain component of object detector while leaving others manually designed. We identify the inconsistency between searched component and manually designed ones would withhold the detector of stronger performance. To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner. In addition, we empirically reveal that different parts of the detector prefer different operators. Motivated by this, we employ a novel scheme to automatically screen different sub search spaces for different components so as to perform the end-to-end search for each component on the corresponding sub search space efficiently. Without bells and whistles, our searched architecture, namely Hit-Detector, achieves 41.4\% mAP on COCO minival set with 27M parameters. Our implementation is available at https://github.com/ggjy/HitDet.pytorch.

An Expert Perspective on "Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection"

The paper explores the application of Neural Architecture Search (NAS) to develop an innovative framework, termed the Hit-Detector, aimed at enhancing object detection. Unlike traditional NAS approaches that focus on a singular component of the object detector, this paper proposes a holistic approach—referred to as the hierarchical trinity architecture search—to concurrently optimize all vital components (backbone, neck, and head) of an object detection system.

Key Contributions

1. Hierarchical Trinity Search Framework: The paper introduces an end-to-end framework that ensures all components of an object detection architecture are optimized in unison. This paradigm addresses the issue of inconsistency arising from the isolated optimization of individual components, as seen in prior works.
2. Component-Specific Search Spaces: A significant insight from the paper reveals that different components in the object detection pipeline favor different operations. This insight leads to a thoughtful stratification of search spaces tailored to the distinctive operational preferences of the backbone, neck, and head.
3. Monumental Performance Improvements: The Hit-Detector achieves a state-of-the-art performance with a 41.4% mAP on the COCO minival set using 27M parameters, underscoring the efficacy of the proposed method in leveraging component-specific search spaces.

Numerical Comparative Analysis

The paper presents substantial empirical results illustrating that the devised architecture outperforms various existing methods in object detection. For instance, when compared to DetNAS and NAS-FPN, which individually optimize the backbone and neck, respectively, the Hit-Detector showcases superior performance by achieving a balance between computational efficiency and detection accuracy. The architecture comprises only 27.12 million parameters with competitive FLOPs, making it both effective and computationally efficient.

Implications for Future Research

The implications of this work are noteworthy for both practical and theoretical domains. Practically, the robustness and efficiency of the Hit-Detector can spearhead advancements in real-world applications like autonomous driving and surveillance, which require swift and precise object detection. Theoretically, this work opens new avenues in NAS research, particularly in joint optimization strategies across multiple components of complex neural architectures.

Speculative Outlook

Given the flexibility and enhanced detection capabilities demonstrated by the Hit-Detector, future research may explore extending this hierarchical framework to other multi-component systems beyond object detection. Moreover, the introduction of adaptive and potentially dynamic search spaces that evolve with real-time data could further augment the adaptability and performance of these systems.

In summary, the Hit-Detector marks a notable progression in the field of automated architecture design for computer vision tasks. By effectively addressing component interplay and proposing a holistic search mechanism, the paper lays down a formidable foundation for subsequent innovations in neural architecture search methodologies.