Papers › Do Different Tracking Tasks Require Different Appearance Models?

Do Different Tracking Tasks Require Different Appearance Models?

5 Jul 2021NeurIPS 2021 12arXiv:2107.02156archive 2025-07-28

Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto

Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too, and now novel approaches proposed by the community are usually specialised to fit only one specific setup. To understand to what extent this specialisation is necessary, in this work we present UniTrack, a solution to address five different tasks within the same framework. UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple ``heads'' that address individual tasks and do not require training. We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered. The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus extending their evaluation and comparison to a larger variety of important problems.

PaperPDFConference PDFCodeCode 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="2107.02156")

Code

Syntology Ran 6 of 17 code samples harvested from 1 repository linked to this paper; 11 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 4 ran with no contract checked.

By repository: official repository: 17 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

Zhongdao/UniTrack officialmentioned in papermentioned on GitHubpytorch 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

17 samples harvested; 6 ran; 0 honoured the contract we drafted; 11 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.

2ran · our draft was wrong
4ran
11unverified

Licence: 0 of the 17 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 Zhongdao/UniTrack. “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.

From3D Zhongdao/UniTrack/model/model.py official repository ran MIT (permissive) · 6bed795c8790b42f · report
HighResolutionModule Zhongdao/UniTrack/model/model.py official repository ran MIT (permissive) · 6cb64253bc2483ab · report
RandomFeatGenerator Zhongdao/UniTrack/model/model.py official repository ran fingerprinted MIT (permissive) · 24f302b7add67f84 · report
ResNet Zhongdao/UniTrack/model/model.py official repository ran fingerprinted MIT (permissive) · 5fb6217486f7e99c · report
_resnet Zhongdao/UniTrack/model/model.py official repository ran · our draft was wrong MIT (permissive) · 8a5d9f9460c91bb5 · report
dets2obs Zhongdao/UniTrack/demo/mot_demo.py official repository ran · our draft was wrong MIT (permissive) · 93ff6b09d3e38b8e · report
HighResolutionNet Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · fe69a6b9983c5ce3 · report
get_cls_net Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · ec2a972e12e2c2f6 · report
load_tc_model Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · bb753455f7400311 · report
load_uvc_model Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · 407da3ab43d8ba3f · report
make_encoder Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · d3b2fda956486d2c · report
partial_load Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · c0367497ab1f7faa · report
resnet101 Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · 33b995998b62c580 · report
resnet18 Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · 08e568179967e865 · report
resnet50 Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · 92b061ec78263602 · report
resnext101_32x8d Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · bf216e79a1ef8b76 · report
resnext50_32x4d Zhongdao/UniTrack/model/model.py official repository unverified MIT (permissive) · 8cc2e400c38e8730 · report

Tasks

Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple People TrackingObject TrackingOnline Multi-Object TrackingPose EstimationPose PredictionPose TrackingSemi-Supervised Video Object SegmentationVideo Instance SegmentationVideo Object SegmentationVideo Object TrackingVisual Object TrackingVisual Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Object Tracking MOT16 UniTrack IDF1 71.8 #6 of 24 Archive leaderboard report
Multi-Object Tracking MOT16 UniTrack IDs 683 #6 of 24 Archive leaderboard report
Multi-Object Tracking MOT16 UniTrack MOTA 74.7 #6 of 24 Archive leaderboard report
Multi-Object Tracking MOTS20 UniTrack IDF1 67.2 #2 of 6 Archive leaderboard report
Multi-Object Tracking MOTS20 UniTrack IDs 622 #2 of 6 Archive leaderboard report
Multi-Object Tracking MOTS20 UniTrack sMOTSA 68.9 #2 of 6 Archive leaderboard report
Pose Estimation J-HMDB UniTrack_i18 Mean PCK@0.1 58.3 #5 of 5 Archive leaderboard report
Pose Estimation J-HMDB UniTrack_i18 Mean PCK@0.2 80.5 #5 of 5 Archive leaderboard report
Pose Tracking PoseTrack2018 UniTrack IDF1 73.2 #2 of 5 Archive leaderboard report
Pose Tracking PoseTrack2018 UniTrack IDs 6760 #2 of 5 Archive leaderboard report
Pose Tracking PoseTrack2018 UniTrack MOTA 63.5 #2 of 5 Archive leaderboard report
Video Instance Segmentation YouTube-VIS validation UniTrack mask AP 30.1 #42 of 44 Archive leaderboard report
Video Object Segmentation DAVIS 2017 UniTrack mIoU 58.4 #4 of 5 Archive leaderboard report
Visual Object Tracking OTB-2015 UniTrack_DCF AUC 0.618 #16 of 18 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