A Turbo-Inference Strategy for Object Detection and Instance Segmentation

• URL: http://arxiv.org/abs/2606.12371v1
• Date: Wed, 10 Jun 2026 17:38:22 GMT
• Title: A Turbo-Inference Strategy for Object Detection and Instance Segmentation
• Abstract summary: We present a turbo-inference strategy for the top-down methods that leverages the complementary information between detection and segmentation tasks iteratively.<n>Our method substantially enhances both detection and segmentation accuracies with a certain increase in computational cost.
• Score: 26.06274613857013
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding [56.079013202051094]
  We present SegVG, a novel method transfers the box-level annotation as signals to provide an additional pixel-level supervision for Visual Grounding. This approach allows us to iteratively exploit the annotation as signals for both box-level regression and pixel-level segmentation.
  arXiv  Detail & Related papers  (2024-07-03T15:30:45Z)
• A Simple Framework for Open-Vocabulary Segmentation and Detection [85.21641508535679]
  We present OpenSeeD, a simple Open-vocabulary and Detection framework that jointly learns from different segmentation and detection datasets. We first introduce a pre-trained text encoder to encode all the visual concepts in two tasks and learn a common semantic space for them. After pre-training, our model exhibits competitive or stronger zero-shot transferability for both segmentation and detection.
  arXiv  Detail & Related papers  (2023-03-14T17:58:34Z)
• A Multi-task Framework for Infrared Small Target Detection and Segmentation [9.033048310220346]
  We propose a novel end-to-end framework for infrared small target detection and segmentation. We use UNet as the backbone to maintain resolution and semantic information. We develop a multi-task framework for infrared small target detection and segmentation.
  arXiv  Detail & Related papers  (2022-06-14T15:43:34Z)
• SOLO: A Simple Framework for Instance Segmentation [84.00519148562606]
  "instance categories" assigns categories to each pixel within an instance according to the instance's location. "SOLO" is a simple, direct, and fast framework for instance segmentation with strong performance. Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy.
  arXiv  Detail & Related papers  (2021-06-30T09:56:54Z)
• Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation [95.74244714914052]
  Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich-temporal information online. PCAN outperforms current video instance tracking and segmentation competition winners on Youtube-VIS and BDD100K datasets.
  arXiv  Detail & Related papers  (2021-06-22T17:57:24Z)
• Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images [58.914034295184685]
  Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region.
  arXiv  Detail & Related papers  (2021-06-14T04:35:36Z)
• SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation [111.61261419566908]
  Deep neural networks (DNNs) are usually trained on a closed set of semantic classes. They are ill-equipped to handle previously-unseen objects. detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving.
  arXiv  Detail & Related papers  (2021-04-30T07:58:19Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)
• Deep Variational Instance Segmentation [7.334808870313923]
  State-of-the-art algorithms often employ two separate stages, the first one generating object proposals and the second one recognizing and refining the boundaries. We propose a novel algorithm that directly utilizes a fully convolutional network (FCN) to predict instance labels.
  arXiv  Detail & Related papers  (2020-07-22T17:57:49Z)
• BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation [30.008473359758632]
  Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. We propose a novel deep panoptic segmentation scheme based on a bidirectional learning pipeline. The experimental results on COCO panoptic benchmark validate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2020-03-31T08:57:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.