Human Instance Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (16 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Pose2Seg: Detection Free Human Instance Segmentation
  28 Mar 2018 7 repositories listed Syntology ran 0 of 16 samples · 16 unverified
  We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
• Detection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous Circle
  2 Dec 2024 1 repository listed
  We condition pose estimation model by segmentation masks instead of bounding boxes to improve instance separation.
• Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes
  16 Jul 2024 1 repository listed
  In computer vision, object detection is an important task that finds its application in many scenarios.
• You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
  9 Dec 2023 1 repository listed
  Human-centric perception (e.
• Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation
  7 Oct 2022 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  In our work, we propose a simple yet effective data-centric approach, Occlusion Copy & Paste, to introduce occluded examples to models during training - we tailor the general copy & paste augmentation approach to tackle…
• Occlusion-Aware Instance Segmentation via BiLayer Network Architectures
  8 Aug 2022 1 repository listed
  Unlike previous instance segmentation methods, we model image formation as a composition of two overlapping layers, and propose Bilayer Convolutional Network (BCNet), where the top layer detects occluding objects…
• Contextual Instance Decoupling for Robust Multi-Person Pose Estimation
  1 Jan 2022 1 repository listed
  Instead of relying on person bounding boxes to spatially differentiate persons, CID decouples persons in an image into multiple instance-aware feature maps.
• Real-time Human-Centric Segmentation for Complex Video Scenes
  16 Aug 2021 1 repository listed
  To alleviate this problem, we propose a mechanism named Inner Center Sampling to improve the accuracy of instance segmentation.
• Fashion-Guided Adversarial Attack on Person Segmentation
  17 Apr 2021 1 repository listed
  It generates adversarial textures learned from fashion style images and then overlays them on the clothing regions in the original image to make all persons in the image invisible to person segmentation networks.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.