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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
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding AttacksCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
Domain Adaptive Faster R-CNN for Object Detection in the WildCode1
Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized DetectionCode1
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic MaskingCode1
Identification of Novel Classes for Improving Few-Shot Object DetectionCode1
Improved Region Proposal Network for Enhanced Few-Shot Object DetectionCode1
Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and TransferabilityCode1
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)Code1
SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface ConditionsCode1
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