Amodal 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

2 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

15 shown of 15 papers with code (24 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.

• Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
  24 Apr 2018 2 repositories listed Syntology ran 8 of 17 samples · 9 unverified · 10 pointer-only (licence)
  Semantic amodal segmentation is a recently proposed extension to instance-aware segmentation that includes the prediction of the invisible region of each object instance.
• Foundation Models for Amodal Video Instance Segmentation in Automated Driving
  21 Sep 2024 1 repository listed
  This way, while basing our method on an amodal instance segmentation, we nevertheless obtain video-level amodal instance segmentation results.
• ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation
  18 Mar 2024 1 repository listed
  Consequently, this compromised quality of visible features during the subsequent visible-to-amodal transition.
• OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts
  16 Jan 2024 1 repository listed
  However, based on ``segmentation within bounding box'' paradigm, current instance segmentation methods using OBBs are overly dependent on bounding box detection performance.
• SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes
  14 Jul 2023 1 repository listed
  In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal…
• Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
  12 Mar 2023 1 repository listed
  Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging.
• AISFormer: Amodal Instance Segmentation with Transformer
  12 Oct 2022 1 repository listed
  AISFormer explicitly models the complex coherence between occluder, visible, amodal, and invisible masks within an object's regions of interest by treating them as learnable queries.
• WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery
  1 Jan 2022 1 repository listed
  Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions.
• Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
  23 Sep 2021 1 repository listed
  Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment.
• A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
  23 Aug 2021 1 repository listed
  The resulting predictions on training images are taken as the pseudo-ground truth for the standard training of Mask-RCNN, which we use for amodal instance segmentation of test images.
• Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
  23 Mar 2021 1 repository listed Syntology ran 7 of 12 samples · 5 unverified
  Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries.
• Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
  25 Oct 2020 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries.
• Layered Embeddings for Amodal Instance Segmentation
  14 Feb 2020 1 repository listed
  The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts.
• Amodal Instance Segmentation With KINS Dataset
  1 Jun 2019 1 repository listed
  We propose the network structure to reason invisible parts via a new multi-task framework with Multi-View Coding (MVC), which combines information in various recognition levels.
• Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
  30 May 2019 1 repository listed
  Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation instead of the commonly used masks and heatmaps.

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.