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 1120 of 90 papers

TitleStatusHype
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer LearningCode1
On the Robustness of Object Detection Models on Aerial ImagesCode1
Improved Region Proposal Network for Enhanced Few-Shot Object DetectionCode1
Identification of Novel Classes for Improving Few-Shot Object DetectionCode1
Towards Scene Understanding for Autonomous Operations on Airport ApronsCode1
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)Code1
Robust Object Detection With Inaccurate Bounding BoxesCode1
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object DetectionCode1
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic SegmentationCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
Show:102550
← PrevPage 2 of 9Next →

No leaderboard results yet.