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 3140 of 90 papers

TitleStatusHype
FROD: Robust Object Detection for Free0
Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving0
COCO-O: A Benchmark for Object Detectors under Natural Distribution ShiftsCode2
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection0
SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection0
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
Mind the Backbone: Minimizing Backbone Distortion for Robust Object DetectionCode0
Identification of Novel Classes for Improving Few-Shot Object DetectionCode1
Towards Scene Understanding for Autonomous Operations on Airport ApronsCode1
Show:102550
← PrevPage 4 of 9Next →

No leaderboard results yet.