RecursiveDet: End-to-End Region-based Recursive Object Detection (2307.13619v1)

Abstract: End-to-end region-based object detectors like Sparse R-CNN usually have multiple cascade bounding box decoding stages, which refine the current predictions according to their previous results. Model parameters within each stage are independent, evolving a huge cost. In this paper, we find the general setting of decoding stages is actually redundant. By simply sharing parameters and making a recursive decoder, the detector already obtains a significant improvement. The recursive decoder can be further enhanced by positional encoding (PE) of the proposal box, which makes it aware of the exact locations and sizes of input bounding boxes, thus becoming adaptive to proposals from different stages during the recursion. Moreover, we also design centerness-based PE to distinguish the RoI feature element and dynamic convolution kernels at different positions within the bounding box. To validate the effectiveness of the proposed method, we conduct intensive ablations and build the full model on three recent mainstream region-based detectors. The RecusiveDet is able to achieve obvious performance boosts with even fewer model parameters and slightly increased computation cost. Codes are available at https://github.com/bravezzzzzz/RecursiveDet.

• The paper demonstrates that using a recursive decoder structure reduces model parameters while maintaining or improving detection accuracy.
• It introduces positional encoding that combines bounding box and centerness cues to enhance dynamic convolutions in object detection.
• Results on COCO 2017 show significant performance gains and reduced computational costs across various backbones.

RecursiveDet: End-to-End Region-based Recursive Object Detection

The paper "RecursiveDet: End-to-End Region-based Recursive Object Detection" presents an innovative approach to enhancing region-based object detectors, particularly Sparse R-CNN, AdaMixer, and DiffusionDet. The proposed solution, RecursiveDet, emphasizes efficiency by reducing model size and computational cost while maintaining or improving detection performance. This summary will discuss the primary contributions and experimental outcomes reported in the paper.

Core Innovations and Methodology

1. Recursive Decoder Structure: RecursiveDet introduces a recursive decoder structure where the traditional multiple cascade stages in region-based detectors are replaced by a single recursive module. By sharing parameters across different stages, RecursiveDet effectively minimizes the model size, making it more resource-efficient without a significant performance trade-off. The paper highlights that the redundancy in the model parameters, particularly in dynamic layers, is addressed effectively through this parameter-sharing strategy.
2. Positional Encoding (PE) Enhancement: The recursive decoder benefits from positional encoding of proposal boxes. This encoding allows the decoder to be aware of the exact location and size of input bounding boxes. RecursiveDet incorporates both global and local position encoding by employing a bounding box PE and centerness-based PE. The latter distinguishes feature elements within a region of interest (RoI) feature set, thus improving the adaptability and effectiveness of dynamic convolutions.
3. Dynamic Layer Utilization: RecursiveDet extends the utility of dynamic layers within the decoder by employing them recursively within the same stage. This recursive use increases model depth without incurring additional parameter costs. The application of dynamic convolutions across more layers within the decoder stage contributes to enhanced detection performance.

Experimental Results

The experimental evaluation conducted on the COCO 2017 dataset demonstrates the efficacy of RecursiveDet. Notably, RecursiveDet achieves notable performance improvements across different region-based detectors while reducing the number of parameters. Some key results include:

• When implemented on Sparse R-CNN with a ResNet-50 backbone, RecursiveDet exceeds the baseline performance by 1.5 AP (Average Precision), while reducing model parameters from 106M to 55M.
• With Swin-B backbone, RecursiveDet achieves an impressive AP of 53.1 on the COCO test-dev set, illustrating its adaptability to different backbone architectures.

Overall, RecursiveDet enables substantial performance gains and parameter reductions compared to other state-of-the-art region-based detectors on COCO 2017, making it an efficient solution for object detection tasks.

Implications and Future Perspectives

The RecursiveDet method provides several implications for the field of object detection. First, it exemplifies how recursive structures can be leveraged to minimize complexity while maintaining or improving performance in complex detection tasks. Second, the integration of positional encodings tailored to region-based detection offers a pathway for future explorations into more sophisticated encoding schemes that could further enhance detection accuracy.

Future developments may explore optimized recursive configurations and further enhancements in dynamic layer efficiency to tackle more complex datasets or applications requiring real-time detection with reduced computational resources. Additionally, extending RecursiveDet to integrate seamlessly with other modern detection architectures could broaden its applicability and impact across various domains.

In summary, RecursiveDet presents a compelling advancement for the computer vision community, offering an efficient and effective pathway for enhancing region-based object detection.