Papers › Navigating Open Set Scenarios for Skeleton-based Action Recognition

Navigating Open Set Scenarios for Skeleton-based Action Recognition

11 Dec 2023arXiv:2312.06330archive 2025-07-28

Kunyu Peng, Cheng Yin, Junwei Zheng, Ruiping Liu, David Schneider, Jiaming Zhang, Kailun Yang, M. Saquib Sarfraz, Rainer Stiefelhagen, Alina Roitberg

In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. However, using pure skeleton data in such open-set conditions poses challenges due to the lack of visual background cues and the distinct sparse structure of body pose sequences. In this paper, we tackle the unexplored Open-Set Skeleton-based Action Recognition (OS-SAR) task and formalize the benchmark on three skeleton-based datasets. We assess the performance of seven established open-set approaches on our task and identify their limits and critical generalization issues when dealing with skeleton information. To address these challenges, we propose a distance-based cross-modality ensemble method that leverages the cross-modal alignment of skeleton joints, bones, and velocities to achieve superior open-set recognition performance. We refer to the key idea as CrossMax - an approach that utilizes a novel cross-modality mean max discrepancy suppression mechanism to align latent spaces during training and a cross-modality distance-based logits refinement method during testing. CrossMax outperforms existing approaches and consistently yields state-of-the-art results across all datasets and backbones. The benchmark, code, and models will be released at https://github.com/KPeng9510/OS-SAR.

PaperPDFCodeCode 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="2312.06330")

Code

Syntology Ran 8 of 12 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 6 ran with no contract checked.

By repository: official repository: 12 samples from 1 repository, 8 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

kpeng9510/os-sar officialmentioned in papermentioned on GitHubpytorchNOASSERTION 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

12 samples harvested; 8 ran; 0 honoured the contract we drafted; 4 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 · our draft was wrong
6ran
4unverified

Licence: 12 of the 12 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 kpeng9510/os-sar. “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.

FeedForward kpeng9510/os-sar/model/temperature_prediction.py official repository ran · our draft was wrong no licence file found · pointer only · 7ad86819c80a33c6 · report
MLPMixer kpeng9510/os-sar/model/temperature_prediction.py official repository ran licence not identified · pointer only · 8223a5e25493a142 · report
downsample kpeng9510/os-sar/feeders/tools.py official repository ran no licence file found · pointer only · 8b14974e8aa7320f · report
eval_osr kpeng9510/os-sar/LogitsCalib.py official repository ran fingerprinted licence not identified · pointer only · 83b675e1ae93be8e · report
get_mapping kpeng9510/os-sar/feeders/feeder_ntu.py official repository ran fingerprinted licence not identified · pointer only · af67c70987fb3f26 · report
get_mapping_seen kpeng9510/os-sar/feeders/feeder_ntu.py official repository ran fingerprinted licence not identified · pointer only · 7d3044eec026734f · report
import_class kpeng9510/os-sar/model/baseline.py official repository ran · our draft was wrong no licence file found · pointer only · ffed4f85d50832c9 · report
valid_crop_resize kpeng9510/os-sar/feeders/tools.py official repository ran no licence file found · pointer only · 37939bae213ecfd4 · report
joint_courruption kpeng9510/os-sar/feeders/augmentations.py official repository unverified licence not identified · pointer only · 6ad3d2fb5ec3d0a9 · report
pose_augmentation kpeng9510/os-sar/feeders/augmentations.py official repository unverified licence not identified · pointer only · fcae67af1dbd3664 · report
temporal_cropresize kpeng9510/os-sar/feeders/augmentations.py official repository unverified licence not identified · pointer only · 0d85210cf685e637 · report
temporal_slice kpeng9510/os-sar/feeders/tools.py official repository unverified no licence file found · pointer only · 2f7f389f0584c97e · report

Tasks

Action RecognitionActivity RecognitionNovelty DetectionOpen Set Action RecognitionOpen Set LearningOut-of-Distribution DetectionSkeleton Based Action Recognitioncross-modal alignment

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

ALIGN

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