SOLO: Segmenting Objects by Locations

Wang, Xinlong; Kong, Tao; Shen, Chunhua; Jiang, Yuning; Li, Lei

doi:10.1007/978-3-030-58523-5_38

Cited by 550 publications

(373 citation statements)

References 23 publications

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“…We further develop a more unified algorithm by combining decoupled SOLO [43] and dynamic SOLO [44]. Specifically, based on dynamic SOLO, we further decouple the mask representation inspired by decoupled SOLO.…”

Section: Decoupled Dynamic Solomentioning

confidence: 99%

The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

Wang

Liu

Kang

et al. 2020

Lecture Notes in Computer Science

124

106

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Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that has made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the "detect-then-segment" strategy (e.g., Mask R-CNN), or predict embedding vectors first then cluster pixels into individual instances. In this paper, we view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location. With this notion, we propose segmenting objects by locations (SOLO), a simple, direct, and fast framework for instance segmentation with strong performance. We derive a few SOLO variants (e.g., Vanilla SOLO, Decoupled SOLO, Dynamic SOLO) following the basic principle. Our method directly maps a raw input image to the desired object categories and instance masks, eliminating the need for the grouping post-processing or the bounding box detection. Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy, while being considerably simpler than the existing methods. Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation. We further demonstrate the flexibility and high-quality segmentation of SOLO by extending it to perform one-stage instance-level image matting.

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Section: Decoupled Dynamic Solomentioning

confidence: 99%

The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

Wang

Liu

Kang

et al. 2020

Lecture Notes in Computer Science

124

106

View full text Add to dashboard Cite

show abstract

“…YOLACT (Bolya et al, 2019a) and YOLACT++ (Bolya et al, 2019b) addresses the segmentation speed at the cost of a reduction in AP by prototyping the masks and producing the instance masks with previously predicted mask coefficients. Most recent methods SOLO (Wang et al, 2020a) and SOLOv2 (Wang et al, 2020b) that addresses both speed and AP provides a simple, fast yet strong segmentation framework. This framework follows a rather unconventional approach to assign each pixel a "instance-category" to modify segmentation into a classification-solvable problem.…”

Section: Discussionmentioning

confidence: 99%

Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests

Adke

Mogel²,

et al. 2021

Front. Artif. Intell.

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The Genetically Modified (GMO) Corn Experiment was performed to test the hypothesis that wild animals prefer Non-GMO corn and avoid eating GMO corn, which resulted in the collection of complex image data of consumed corn ears. This study develops a deep learning-based image processing pipeline that aims to estimate the consumption of corn by identifying corn and its bare cob from these images, which will aid in testing the hypothesis in the GMO Corn Experiment. Ablation uses mask regional convolutional neural network (Mask R-CNN) for instance segmentation. Based on image data annotation, two approaches for segmentation were discussed: identifying whole corn ears and bare cob parts with and without corn kernels. The Mask R-CNN model was trained for both approaches and segmentation results were compared. Out of the two, the latter approach, i.e., without the kernel, was chosen to estimate the corn consumption because of its superior segmentation performance and estimation accuracy. Ablation experiments were performed with the latter approach to obtain the best model with the available data. The estimation results of these models were included and compared with manually labeled test data with R2 = 0.99 which showed that use of the Mask R-CNN model to estimate corn consumption provides highly accurate results, thus, allowing it to be used further on all collected data and help test the hypothesis of the GMO Corn Experiment. These approaches may also be applied to other plant phenotyping tasks (e.g., yield estimation and plant stress quantification) that require instance segmentation.

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“…Recently, significant advances have been made in single-stage network architectures for instance segmentation. SOLOv2 [34,35] is one leading example of a network design which is simpler than Mask R-CNN while reporting similar performance. Fig 4 shows the basic structure of the SOLOv2 network.…”

Section: Networkmentioning

confidence: 99%

Object Detection and Instance Segmentation in Chest X-rays for Tuberculosis Screening

et al. 2021

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Tuberculosis (TB) is a highly contagious disease leading to the deaths of approximately 2 million people annually. TB primarily affects the lungs and is spread through the air when people cough, sneeze, or spit. Providing healthcare professionals with better information, at a faster pace, is essential for combating this disease, especially in Low and Middle Income Countries (LMICs) with resource-constrained health systems. In this paper we describe how using convolution neural networks (CNNs) with an object level annotated dataset of chest X-rays (CXRs) allows us to identify the location of pulmonary issues indicative of TB. We compare the performance of Faster R-nobreakdash-CNN, Mask R-nobreakdash-CNN, Cascade versions of each, and SOLOv2, demonstrating reasonable results with a small dataset. We present a method to reduce the false positive rate by comparing the location of a detected object with the known location of areas where the detected class is likely to occur in the lung. Our results show that object detection and instance segmentation of CXRs can be achieved with a dataset of high-quality, object level annotations, and could be used as part of an automated TB screening process. This work has the potential to improve the speed of TB diagnosis in LMICs, if properly integrated into the healthcare system and adapted to existing clinical workflows and local regulations.

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SOLO: Segmenting Objects by Locations

Cited by 550 publications

References 23 publications

The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests

Object Detection and Instance Segmentation in Chest X-rays for Tuberculosis Screening

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