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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 3140 of 90 papers

TitleStatusHype
Robust Object Detection via Instance-Level Temporal Cycle ConfusionCode1
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-DistillationCode1
On the Robustness of Object Detection Models on Aerial ImagesCode1
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household RoboticsCode1
Exploring Sequence Feature Alignment for Domain Adaptive Detection TransformersCode1
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial ImagesCode1
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective WhiteningCode1
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland WatersCode1
YOLOv3: An Incremental ImprovementCode1
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