{
  "id": 224873,
  "title": "💥 💥 Deep Learning Research Papers for Image Segmentation 🔥🔥",
  "url": "/competitions/hubmap-kidney-segmentation/discussion/224873",
  "author_name": "Tensor Girl",
  "post_date": "2021-03-10T03:41:45.600000",
  "votes": 17,
  "comment_count": 0,
  "views": 0,
  "content": "<p>I have curated the deep learning research papers for image segmentation which has been released in top conferences in recent years </p>\n<p><img src=\"https://drive.google.com/uc?id=1FKva37exs_Rd8rafd1n3ioxiXfXggCAp\" alt=\"\"></p>\n<p><strong>HIERARCHICAL MULTI-SCALE ATTENTION FOR SEMANTIC SEGMENTATION</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2005.10821v1.pdf\" target=\"_blank\">https://arxiv.org/pdf/2005.10821v1.pdf</a></p>\n<p><strong>Object-Contextual Representations for Semantic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1909.11065v5.pdf\" target=\"_blank\">https://arxiv.org/pdf/1909.11065v5.pdf</a></p>\n<p><strong>EfficientPS: Efficient Panoptic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2004.02307v3.pdf\" target=\"_blank\">https://arxiv.org/pdf/2004.02307v3.pdf</a></p>\n<p><strong>A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1911.10194v3.pdf\" target=\"_blank\">https://arxiv.org/pdf/1911.10194v3.pdf</a></p>\n<p><strong>Object-Contextual Representations for Semantic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1909.11065v5.pdf\" target=\"_blank\">https://arxiv.org/pdf/1909.11065v5.pdf</a></p>\n<p><strong>DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2003.11883v1.pdf\" target=\"_blank\">https://arxiv.org/pdf/2003.11883v1.pdf</a></p>\n<p><strong>ResNeSt: Split-Attention Networks</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2004.08955v2.pdf\" target=\"_blank\">https://arxiv.org/pdf/2004.08955v2.pdf</a></p>\n<p><strong>Gated-SCNN: Gated Shape CNNs for Semantic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1907.05740v1.pdf\" target=\"_blank\">https://arxiv.org/pdf/1907.05740v1.pdf</a></p>\n<p>Hope you found this useful . If you come across any interesting research papers , do share in the comment and I will update the post</p>\n<p>Good Luck to the competition !!!</p>",
  "messages": [
    {
      "id": 1232935,
      "postDate": "2021-03-10T03:41:45.600Z",
      "content": "<p>I have curated the deep learning research papers for image segmentation which has been released in top conferences in recent years </p>\n<p><img src=\"https://drive.google.com/uc?id=1FKva37exs_Rd8rafd1n3ioxiXfXggCAp\" alt=\"\"></p>\n<p><strong>HIERARCHICAL MULTI-SCALE ATTENTION FOR SEMANTIC SEGMENTATION</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2005.10821v1.pdf\" target=\"_blank\">https://arxiv.org/pdf/2005.10821v1.pdf</a></p>\n<p><strong>Object-Contextual Representations for Semantic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1909.11065v5.pdf\" target=\"_blank\">https://arxiv.org/pdf/1909.11065v5.pdf</a></p>\n<p><strong>EfficientPS: Efficient Panoptic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2004.02307v3.pdf\" target=\"_blank\">https://arxiv.org/pdf/2004.02307v3.pdf</a></p>\n<p><strong>A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1911.10194v3.pdf\" target=\"_blank\">https://arxiv.org/pdf/1911.10194v3.pdf</a></p>\n<p><strong>Object-Contextual Representations for Semantic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1909.11065v5.pdf\" target=\"_blank\">https://arxiv.org/pdf/1909.11065v5.pdf</a></p>\n<p><strong>DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2003.11883v1.pdf\" target=\"_blank\">https://arxiv.org/pdf/2003.11883v1.pdf</a></p>\n<p><strong>ResNeSt: Split-Attention Networks</strong></p>\n<p><a href=\"https://arxiv.org/pdf/2004.08955v2.pdf\" target=\"_blank\">https://arxiv.org/pdf/2004.08955v2.pdf</a></p>\n<p><strong>Gated-SCNN: Gated Shape CNNs for Semantic Segmentation</strong></p>\n<p><a href=\"https://arxiv.org/pdf/1907.05740v1.pdf\" target=\"_blank\">https://arxiv.org/pdf/1907.05740v1.pdf</a></p>\n<p>Hope you found this useful . If you come across any interesting research papers , do share in the comment and I will update the post</p>\n<p>Good Luck to the competition !!!</p>",
      "rawMarkdown": "I have curated the deep learning research papers for image segmentation which has been released in top conferences in recent years \n\n![](https://drive.google.com/uc?id=1FKva37exs_Rd8rafd1n3ioxiXfXggCAp)\n\n**HIERARCHICAL MULTI-SCALE ATTENTION FOR SEMANTIC SEGMENTATION**\n\nhttps://arxiv.org/pdf/2005.10821v1.pdf\n\n**Object-Contextual Representations for Semantic Segmentation**\n\nhttps://arxiv.org/pdf/1909.11065v5.pdf\n\n**EfficientPS: Efficient Panoptic Segmentation**\n\nhttps://arxiv.org/pdf/2004.02307v3.pdf\n\n**A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation**\n\nhttps://arxiv.org/pdf/1911.10194v3.pdf\n\n**Object-Contextual Representations for Semantic Segmentation**\n\nhttps://arxiv.org/pdf/1909.11065v5.pdf\n\n**DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation**\n\nhttps://arxiv.org/pdf/2003.11883v1.pdf\n\n**ResNeSt: Split-Attention Networks**\n\nhttps://arxiv.org/pdf/2004.08955v2.pdf\n\n**Gated-SCNN: Gated Shape CNNs for Semantic Segmentation**\n\nhttps://arxiv.org/pdf/1907.05740v1.pdf\n\nHope you found this useful . If you come across any interesting research papers , do share in the comment and I will update the post\n\nGood Luck to the competition !!!",
      "votes": 17
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1232935": "I have curated the deep learning research papers for image segmentation which has been released in top conferences in recent years \n\n![](https://drive.google.com/uc?id=1FKva37exs_Rd8rafd1n3ioxiXfXggCAp)\n\n**HIERARCHICAL MULTI-SCALE ATTENTION FOR SEMANTIC SEGMENTATION**\n\nhttps://arxiv.org/pdf/2005.10821v1.pdf\n\n**Object-Contextual Representations for Semantic Segmentation**\n\nhttps://arxiv.org/pdf/1909.11065v5.pdf\n\n**EfficientPS: Efficient Panoptic Segmentation**\n\nhttps://arxiv.org/pdf/2004.02307v3.pdf\n\n**A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation**\n\nhttps://arxiv.org/pdf/1911.10194v3.pdf\n\n**Object-Contextual Representations for Semantic Segmentation**\n\nhttps://arxiv.org/pdf/1909.11065v5.pdf\n\n**DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation**\n\nhttps://arxiv.org/pdf/2003.11883v1.pdf\n\n**ResNeSt: Split-Attention Networks**\n\nhttps://arxiv.org/pdf/2004.08955v2.pdf\n\n**Gated-SCNN: Gated Shape CNNs for Semantic Segmentation**\n\nhttps://arxiv.org/pdf/1907.05740v1.pdf\n\nHope you found this useful . If you come across any interesting research papers , do share in the comment and I will update the post\n\nGood Luck to the competition !!!"
  }
}