AI RESEARCH
COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions
arXiv CS.LG
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ArXi:2606.02603v1 Announce Type: cross Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images. This is not realistic because real cameras often capture blur, sensor noise, weather effects, and compression artifacts. We present COD10K-C, a corruption robustness benchmark based on COD10K. It includes 8 corruption types and 5 severity levels, giving 40 conditions and 81,040 evaluation pairs in total.