Multi-Human Parsing

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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

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

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (11 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.

• Mask R-CNN
  20 Mar 2017 179 repositories listed Syntology ran 42 of 140 samples · 98 unverified · 23 pointer-only (licence)
  Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
• Semantic Instance Segmentation with a Discriminative Loss Function
  8 Aug 2017 8 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be…
• Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
  11 Jul 2019 3 repositories listed
  On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin.
• Parsing R-CNN for Instance-Level Human Analysis
  30 Nov 2018 2 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
• Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
  10 Apr 2018 2 repositories listed
  Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group…
• Multiple-Human Parsing in the Wild
  19 May 2017 2 repositories listed
  To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.
• Instance-aware Semantic Segmentation via Multi-task Network Cascades
  14 Dec 2015 2 repositories listed
  We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.
• UniParser: Multi-Human Parsing with Unified Correlation Representation Learning
  13 Oct 2023 1 repository listed
  Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information.
• Single-stage Multi-human Parsing via Point Sets and Center-based Offsets
  22 Apr 2023 1 repository listed
  We instead present a high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem into two fine-grained sub-problems, i.
• Holistic, Instance-Level Human Parsing
  11 Sep 2017 1 repository listed
  We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to.

Syntology lines on 3 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.