SOTAVerified

Robust Object Detection

A Benchmark for the: Robustness of Object Detection Models to Image Corruptions and Distortions

To allow fair comparison of robustness enhancing methods all models have to use a standard ResNet50 backbone because performance strongly scales with backbone capacity. If requested an unrestricted category can be added later.

Benchmark Homepage: https://github.com/bethgelab/robust-detection-benchmark

Metrics:

mPC [AP]: Mean Performance under Corruption [measured in AP]

rPC [%]: Relative Performance under Corruption [measured in %]

Test sets: Coco: val 2017; Pascal VOC: test 2007; Cityscapes: val;

( Image credit: Benchmarking Robustness in Object Detection )

Papers

Showing 2130 of 90 papers

TitleStatusHype
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic MaskingCode1
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-DistillationCode1
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial ImagesCode1
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial DomainsCode1
SimROD: A Simple Adaptation Method for Robust Object DetectionCode1
Exploring Sequence Feature Alignment for Domain Adaptive Detection TransformersCode1
Robust Object Detection via Instance-Level Temporal Cycle ConfusionCode1
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective WhiteningCode1
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding AttacksCode1
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland WatersCode1
Show:102550
← PrevPage 3 of 9Next →

No leaderboard results yet.