Papers › PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points

PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points

20 Oct 2022arXiv:2210.11035archive 2025-07-28

Jing Tan, Xiaotong Zhao, Xintian Shi, Bin Kang, LiMin Wang

Traditional temporal action detection (TAD) usually handles untrimmed videos with small number of action instances from a single label (e.g., ActivityNet, THUMOS). However, this setting might be unrealistic as different classes of actions often co-occur in practice. In this paper, we focus on the task of multi-label temporal action detection that aims to localize all action instances from a multi-label untrimmed video. Multi-label TAD is more challenging as it requires for fine-grained class discrimination within a single video and precise localization of the co-occurring instances. To mitigate this issue, we extend the sparse query-based detection paradigm from the traditional TAD and propose the multi-label TAD framework of PointTAD. Specifically, our PointTAD introduces a small set of learnable query points to represent the important frames of each action instance. This point-based representation provides a flexible mechanism to localize the discriminative frames at boundaries and as well the important frames inside the action. Moreover, we perform the action decoding process with the Multi-level Interactive Module to capture both point-level and instance-level action semantics. Finally, our PointTAD employs an end-to-end trainable framework simply based on RGB input for easy deployment. We evaluate our proposed method on two popular benchmarks and introduce the new metric of detection-mAP for multi-label TAD. Our model outperforms all previous methods by a large margin under the detection-mAP metric, and also achieves promising results under the segmentation-mAP metric. Code is available at https://github.com/MCG-NJU/PointTAD.

PaperPDFCodeCode Syntology ran

In Syntology 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="2210.11035")

Code

Syntology Ran 6 of 8 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 6 ran with no contract checked.

By repository: official repository: 1 sample from 1 repository, 1 ran; found in paper text by Syntology: 7 samples from 1 repository, 5 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

mcg-nju/pointtad officialmentioned in paperpytorchApache-2.0 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

8 samples harvested; 6 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

6ran
2unverified

Licence: 0 of the 8 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 2 repositories linked to this paper, official or community; each sample names its own and says which. “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.

PointTAD mcg-nju/pointtad/models/pointtad.py official repository ran · metamorphic tier: deterministic Apache-2.0 (permissive) · 17bf002b5ad9eee4 · report
batched_nms PeizeSun/SparseR-CNN/detectron2/layers/nms.py found in paper text by Syntology ran MIT (permissive) · 5a3c7069b28e9ffb · report
convert_basic_c2_names PeizeSun/SparseR-CNN/detectron2/checkpoint/c2_model_loading.py found in paper text by Syntology ran MIT (permissive) · e8526a516b4f9206 · report
convert_c2_detectron_names PeizeSun/SparseR-CNN/detectron2/checkpoint/c2_model_loading.py found in paper text by Syntology ran MIT (permissive) · 52810b504f0d1066 · report
pad_masks PeizeSun/SparseR-CNN/detectron2/layers/mask_ops.py found in paper text by Syntology ran fingerprinted MIT (permissive) · 406f84cf799ed34d · report
paste_mask_in_image_old PeizeSun/SparseR-CNN/detectron2/layers/mask_ops.py found in paper text by Syntology ran MIT (permissive) · d03592674030077f · report
batched_nms_rotated PeizeSun/SparseR-CNN/detectron2/layers/nms.py found in paper text by Syntology unverified MIT (permissive) · af6fcdbdb551e018 · report
nms_rotated PeizeSun/SparseR-CNN/detectron2/layers/nms.py found in paper text by Syntology unverified MIT (permissive) · d3fa37a000ad294d · report

Tasks

Action DetectionTemporal Action Localization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Temporal Action Localization MultiTHUMOS PointTAD Average mAP 23.5 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.1 42.3 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.2 39.7 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.3 35.8 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.4 30.9 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.5 24.9 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.6 18.5 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.7 12.0 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.8 5.6 #5 of 8 Archive leaderboard report
Temporal Action Localization MultiTHUMOS PointTAD mAP IOU@0.9 1.4 #5 of 8 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