mlsanityData to 7 Oct 2026

Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

arXiv 2305.03640 ↗First posted 5 May 2023cs.CVcited by 3

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Abstract (arXiv)

In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://github.com/sanket0707/GNN-Mixer.git

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

20 of 32 references.

References · 20

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  4. 2020Point Transformercited by 1,462
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8 more
  1. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  2. 2022Event Transformer. A sparse-aware solution for efficient event data processingcited by 48
  3. 2022Amodal Panoptic Segmentationcited by 37
  4. 2020TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognitioncited by 15
  5. 2021Neuromorphic Camera Denoising using Graph Neural Network-driven Transformerscited by 15
  6. 2023A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environmentcited by 7
  7. 2023Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Graspingcited by 5
  8. 2022CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Imagecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Networkcited by 4
  2. 2026Efficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidancecited by 0
  3. 2026Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosiscited by 0