Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation (2312.01220v2)

Abstract: Detecting objects in low-light scenarios presents a persistent challenge, as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However, the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge, we propose to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Revisiting Retinex theory in the low-level vision, we first design a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next, an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark, DARK FACE, and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://github.com/ZPDu/DAI-Net.

• The paper presents a novel zero-shot domain adaptation strategy that leverages Retinex-based reflectance learning to enhance object detection in low-light conditions.
• It integrates an interchange-redecomposition-coherence procedure to stabilize illumination-invariant representations, achieving significant performance gains on benchmark datasets.
• The method enables training on well-lit data only, simplifying low-light data challenges and offering practical benefits for applications like autonomous driving and surveillance.

Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation: An Expert Analysis

The paper "Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation" addresses the significant challenge of detecting objects in low-light scenarios, a problem that is exacerbated by the difficulty of collecting and annotating low-light images. The research proposes a novel approach utilizing zero-shot day-night domain adaptation (ZSDA), enabling the generalization of object detectors from well-lit scenarios to low-light conditions without the necessity of real low-light data. This methodology circumvents the limitations posed by the scarcity of low-light datasets.

Research Contributions and Methodology

1. Reflectance Representation Learning Module: The authors revisit the Retinex theory, a foundational approach in low-level vision, to design a reflectance representation learning module. This module, integrated into an object detection pipeline, leverages a novel illumination invariance reinforcement strategy to learn Retinex-based illumination invariance. The module comprises a decoder branch that extracts reflectance (illumination-invariant features) from both well-lit and synthesized low-light images, thereby enhancing the detection model's ability to operate in varying lighting conditions.
2. Interchange-Redecomposition-Coherence Procedure: A significant advancement proposed in the paper is the interchange-redecomposition-coherence procedure. This builds upon the Retinex image decomposition process by performing sequential decompositions and promoting consistency between them through a redecomposition cohering loss. This innovative procedure is designed to further stabilize and refine the illumination-invariant reflectance representation.
3. Experimental Validation: Extensive experiments were conducted on datasets such as ExDark, DARK FACE, and CODaN. The results demonstrate that the proposed method significantly enhances the generalizability of low-light object detection compared to traditional approaches.

Theoretical and Practical Implications

• Illumination-Invariance: By adopting Retinex-based techniques traditionally used in image enhancement, this research pioneers their application in high-level detection tasks. It underscores the potential of leveraging domain-invariant properties for zero-shot adaptation tasks in computer vision.
• Practical Contexts: This methodology is particularly relevant for real-world applications requiring robust object detection in varying lighting conditions, such as autonomous driving and security surveillance. The ability to train models without real low-light data offers substantial savings in data collection efforts and extends the applicability of existing well-lit datasets.

Speculation on Future Developments

The proposed framework opens avenues for future research in zero-shot domain adaptation by demonstrating the viability of Retinex theory in model generalization beyond traditional boundaries. This approach could be expanded to other domains experiencing domain shift due to environmental conditions, such as weather variations or material inconsistencies.

Additionally, future work may explore the integration of more sophisticated neural architectures or transformer-based methods within this context, potentially enhancing the refinement and robustness of learned representations even further. The blend of physics-based approaches with modern deep learning strategies could yield more efficient and adaptive models across a broader range of challenging visual tasks.

In conclusion, the paper makes a significant contribution to the ongoing evolution of object detection methodologies in adverse conditions, promoting a deeper understanding of how zero-shot domain adaptation can be practically and effectively achieved. The reflection on Retinex theory within high-level detection tasks can serve as an inspiration for further innovative adaptations of classical vision concepts in AI.