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1. SRNet: Spatial Relation Network for Efficient Single Stage Instance Segmentation in Videos

   Ying, Xiaowen; Li, Xin; Chuah, Mooi Choo (October 2021, ACM Multimedia 2021)

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   The task of instance segmentation in videos aims to consistently identify objects at pixel level throughout the entire video sequence. Existing state-of-the-art methods either follow the tracking-bydetection paradigm to employ multi-stage pipelines or directly train a complex deep model to process the entire video clips as 3D volumes. However, these methods are typically slow and resourceconsuming such that they are often limited to offline processing. In this paper, we propose SRNet, a simple and efficient framework for joint segmentation and tracking of object instances in videos. The key to achieving both high efficiency and accuracy in our framework is to formulate the instance segmentation and tracking problem into a unified spatial-relation learning task where each pixel in the current frame relates to its object center, and each object center relates to its location in the previous frame. This unified learning framework allows our framework to perform join instance segmentation and tracking through a single stage while maintaining low overheads among different learning tasks. Our proposed framework can handle two different task settings and demonstrates comparable performance with state-of-the-art methods on two different benchmarks while running significantly faster. 

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2. Deep Variational Instance Segmentation

   Yuan, Jialin; Chen, Chao; Li, Fuxin (October 2020, Advances in neural information processing systems)

   null (Ed.)

   Instance segmentation, which seeks to obtain both class and instance labels for each pixel in the input image, is a challenging task in computer vision. State-ofthe-art algorithms often employ a search-based strategy, which first divides the output image with a regular grid and generate proposals at each grid cell, then the proposals are classified and boundaries refined. In this paper, we propose a novel algorithm that directly utilizes a fully convolutional network (FCN) to predict instance labels. Specifically, we propose a variational relaxation of instance segmentation as minimizing an optimization functional for a piecewise-constant segmentation problem, which can be used to train an FCN end-to-end. It extends the classical Mumford-Shah variational segmentation algorithm to be able to handle the permutation-invariant ground truth in instance segmentation. Experiments on PASCAL VOC 2012 and the MSCOCO 2017 dataset show that the proposed approach efficiently tackles the instance segmentation task. The source code and trained models are released at https://github.com/jia2lin3yuan1/2020-instanceSeg. 

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3. PIXEL-WISE NEURAL CELL INSTANCE SEGMENTATION

   Yi, J.; Wu, P.; Hoeppner, D. J.; Metaxas, D. (April 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018))

   Accurate cell instance segmentation plays an important role in the study of neural cell interactions, which are critical for understanding the development of brain. These interactions are performed through the filopodia and lamellipodia of neural cells, which are extremely tiny structures and as a result render most existing instance segmentation methods powerless to precisely capture them. To solve this issue, in this paper we present a novel hierarchical neural network comprising object detection and segmentation modules. Compared to previous work, our model is able to efficiently share and make full use of the information at different levels between the two modules. Our method is simple yet powerful, and experimental results show that it captures the contours of neural cells, especially the filopodia and lamellipodia, with high accuracy, and outperforms recent state of the art by a large margin. 

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4. Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes

   Jingru Yi, Pengxiang Wu (October 2019, MICCAI 2019)

   Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells due to the lack of global understanding of the objects. In contrast, box-based instance segmentation solves this problem by combining object detection with segmentation. However, existing methods typically utilize anchor box-based detectors, which would lead to inferior instance segmentation performance due to the class imbalance issue. In this paper, we propose a new box-based cell instance segmentation method. In particular, we first detect the five pre-defined points of a cell via keypoints detection. Then we group these points according to a keypoint graph and subsequently extract the bounding box for each cell. Finally, cell segmentation is performed on feature maps within the bounding boxes. We validate our method on two cell datasets with distinct object shapes, and empirically demonstrate the superiority of our method compared to other instance segmentation techniques. 

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5. Sketch-to-image synthesis via semantic masks

   https://doi.org/10.1007/s11042-023-16704-z  

   Baraheem, Samah S; Nguyen, Tam V (March 2024, Multimedia Tools and Applications)

   Sketch-to-image is an important task to reduce the burden of creating a color image from scratch. Unlike previous sketch-to-image models, where the image is synthesized in an end-to-end manner, leading to an unnaturalistic image, we propose a method by decomposing the problem into subproblems to generate a more naturalistic and reasonable image. It first generates an intermediate output which is a semantic mask map from the input sketch through instance and semantic segmentation in two levels, background segmentation and foreground segmentation. Background segmentation is formed based on the context of the foreground objects. Then, the foreground segmentations are sequentially added to the created background segmentation. Finally, the generated mask map is fed into an image-to-image translation model to generate an image. Our proposed method works with 92 distinct classes. Compared to state-of-the-art sketch-to-image models, our proposed method outperforms the previous methods and generates better images. 

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