Papers › Hierarchical Open-vocabulary Universal Image Segmentation

Hierarchical Open-vocabulary Universal Image Segmentation

3 Jul 2023NeurIPS 2023 11arXiv:2307.00764archive 2025-07-28

Xudong Wang, Shufan Li, Konstantinos Kallidromitis, Yusuke Kato, Kazuki Kozuka, Trevor Darrell

Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple levels of granularity, introducing inherent segmentation ambiguity. Unlike existing methods that typically sidestep this ambiguity and treat it as an external factor, our approach actively incorporates a hierarchical representation encompassing different semantic-levels into the learning process. We propose a decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff". Additionally, we systematically examine the differences that exist in the textual and visual features between these types of categories. Our resulting model, named HIPIE, tackles HIerarchical, oPen-vocabulary, and unIvErsal segmentation tasks within a unified framework. Benchmarked on over 40 datasets, e.g., ADE20K, COCO, Pascal-VOC Part, RefCOCO/RefCOCOg, ODinW and SeginW, HIPIE achieves the state-of-the-art results at various levels of image comprehension, including semantic-level (e.g., semantic segmentation), instance-level (e.g., panoptic/referring segmentation and object detection), as well as part-level (e.g., part/subpart segmentation) tasks. Our code is released at https://github.com/berkeley-hipie/HIPIE.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2307.00764")

Code

Syntology Ran 2 of 4 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 2 ran · fixture could not drive it.

By repository: official repository: 4 samples from 1 repository, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

berkeley-hipie/hipie officialmentioned in papermentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

4 samples harvested; 2 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

2ran · fixture could not drive it
2unverified

Licence: 0 of the 4 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from berkeley-hipie/hipie. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

get_images_color_similarity berkeley-hipie/hipie/projects/HIPIE/hipie/hipie_img.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 3a4a70eddd103b6f · report
unfold_wo_center berkeley-hipie/hipie/projects/HIPIE/hipie/hipie_img.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 6bc62d34967e2917 · report
convert_grounding_to_od_logits berkeley-hipie/hipie/projects/HIPIE/hipie/hipie_img.py official repository unverified MIT (permissive) · 133372f6b12b6ba0 · report
vote berkeley-hipie/hipie/projects/HIPIE/hipie/demo_lib/demo_utils.py official repository unverified MIT (permissive) · 0d78867cbc2d1619 · report

Tasks

Image ComprehensionImage SegmentationObject DetectionPanoptic SegmentationReferring Expression SegmentationRepresentation LearningSegmentationSemantic SegmentationUniversal SegmentationZero Shot Segmentationobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Segmentation Pascal Panoptic Parts HIPIE (ViT-H) mIoUPartS 63.8 #1 of 4 Archive leaderboard report
Image Segmentation Pascal Panoptic Parts HIPIE (ResNet-50) mIoUPartS 57.2 #3 of 4 Archive leaderboard report
Panoptic Segmentation COCO minival HIPIE (ViT-H, single-scale) PQ 58.1 #12 of 31 Archive leaderboard report
Panoptic Segmentation COCO minival HIPIE (ViT-H, single-scale) mIoU 66.8 #12 of 31 Archive leaderboard report
Referring Expression Segmentation RefCOCO+ val HIPIE Overall IoU 73.9 #7 of 33 Archive leaderboard report
Referring Expression Segmentation RefCoCo val HIPIE Overall IoU 82.8 #5 of 37 Archive leaderboard report
Zero Shot Segmentation Segmentation in the Wild HIPIE Mean AP 41.6 #4 of 12 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections