Papers › Bounding Box Regression with Uncertainty for Accurate Object Detection

Bounding Box Regression with Uncertainty for Accurate Object Detection

23 Sep 2018CVPR 2019 6arXiv:1809.08545archive 2025-07-28

Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, Xiangyu Zhang

Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to merge neighboring bounding boxes during non-maximum suppression (NMS), which further improves the localization performance. On MS-COCO, we boost the Average Precision (AP) of VGG-16 Faster R-CNN from 23.6% to 29.1%. More importantly, for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP90 by 1.8% and 6.2% respectively, which significantly outperforms previous state-of-the-art bounding box refinement methods. Our code and models are available at: github.com/yihui-he/KL-Loss

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="1809.08545")

Code

Syntology Ran 0 of 4 code samples harvested from 2 repositories linked to this paper; 4 have no recorded run.

By repository: official repository: 4 samples from 2 repositories, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

yihui-he/softer-NMS officialmentioned in papermentioned on GitHubcaffe2Apache-2.0 report
yihui-he/KL-Loss officialmentioned in papercaffe2Apache-2.0 report
fiveai/saod mentioned 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

4 samples harvested; 0 ran; 0 honoured the contract we drafted; 4 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.

4unverified

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 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.

add_VGG16_conv5_body yihui-he/KL-Loss/detectron/modeling/VGG16.py official repository unverified Apache-2.0 (permissive) · 867590b1664a9388 · report
add_VGG_CNN_M_1024_conv5_body yihui-he/KL-Loss/detectron/modeling/VGG_CNN_M_1024.py official repository unverified Apache-2.0 (permissive) · a7195f42969e79d3 · report
generate_anchors yihui-he/KL-Loss/detectron/modeling/generate_anchors.py official repository unverified Apache-2.0 (permissive) · 7b2d56f403fb4468 · report
qb yihui-he/softer-NMS/detectron/utils/py_cpu_nms.py official repository unverified Apache-2.0 (permissive) · 9088711bcb0a4082 · report

Tasks

ObjectObject DetectionObject Localizationobject-detectionregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Detection COCO test-dev ResNet-50-FPN Mask R-CNN + KL Loss + var voting + soft-NMS box mAP 40.4 #194 of 225 Archive leaderboard report
Object Detection PASCAL VOC 2007 VGG-16 + KL Loss + var voting + soft-NMS MAP 71.6% #21 of 30 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.

Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionFaster R-CNNGlobal Average PoolingKaiming InitializationMask R-CNNMax PoolingRPNReLUResidual BlockResidual ConnectionRoIAlignRoIPoolSoftmax

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