A Turbo-Inference Strategy for Object Detection and Instance Segmentation

2026-06-10

AI Summary

This work proposes a novel turbo inference strategy that enables bidirectional iterative refinement between detection and segmentation during inference, without requiring retraining. Challenging the conventional unidirectional “detect-then-segment” paradigm in top-down instance segmentation, the method introduces a closed-loop interaction mechanism through turbo detection and segmentation heads, coupled with cross-task feature fusion. This design dynamically leverages complementary information from both tasks while preserving the top-down architecture. Extensive experiments demonstrate significant improvements in both detection and segmentation accuracy on COCO, iFLYTEK, and Cityscapes benchmarks, achieving a favorable trade-off between performance and inference efficiency.

Abstract

Object detection and instance segmentation tasks are closely related. Existing top-down instance segmentation methods usually follow a detect-then-segment paradigm, where an initial detector is used to recognize and localize objects with bounding boxes, followed by the segmentation of an instance mask within each bounding box. In such methods, the detection accuracy directly influences the subsequent segmentation performance. However, previous research has seldom explored the impact of the instance segmentation task on object detection. In this paper, we present a turbo-inference strategy for the top-down methods that leverages the complementary information between detection and segmentation tasks iteratively. Specifically we design two modules: turbo-detection head and turbo-segmentation head, which facilitate communication between the tasks. The two modules form a closed loop that interlaces the detection and segmentation results without retraining the model. Comprehensive experiments on the COCO, iFLYTEK, and Cityscapes datasets demonstrate that our method substantially enhances both detection and segmentation accuracies with a certain increase in computational cost. The proposed method represents a tradeoff between prediction accuracy and inference speed. Codes are available at https://github.com/zhaozhen2333/Turbo-Learning.git.

Problem

• object detection
• instance segmentation
• detect-then-segment paradigm
• task interaction
• top-down methods

Innovation

• turbo-inference
• detect-then-segment
• task interaction
• instance segmentation
• object detection