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Cascade R-CNN

34 papers tagged archive 2025-07-28

Introduced by Zhaowei Cai et al. in Cascade R-CNN: Delving into High Quality Object Detection

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Cascade R-CNN is an object detection architecture that seeks to address problems with degrading performance with increased IoU thresholds (due to overfitting during training and inference-time mismatch between IoUs for which detector is optimal and the inputs). It is a multi-stage extension of the R-CNN, where detector stages deeper into the cascade are sequentially more selective against close false positives. The cascade of R-CNN stages are trained sequentially, using the output of one stage to train the next. This is motivated by the observation that the output IoU of a regressor is almost invariably better than the input IoU.

Cascade R-CNN does not aim to mine hard negatives. Instead, by adjusting bounding boxes, each stage aims to find a good set of close false positives for training the next stage. When operating in this manner, a sequence of detectors adapted to increasingly higher IoUs can beat the overfitting problem, and thus be effectively trained. At inference, the same cascade procedure is applied. The progressively improved hypotheses are better matched to the increasing detector quality at each stage.

PaperSourceSee Code · open-mmlab/mmdetection

Papers archive 2025-07-28

30 shown of 34, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 41 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection23
object-detection18
Object9
Instance Segmentation7
Semantic Segmentation5
Segmentation3
Vocal Bursts Intensity Prediction3
Data Augmentation2
General Classification2
Image Compression2
regression2
2D Object Detection1
Action Detection1
Activity Detection1
Avg1
Classification1
Data Compression1
Data Visualization1
Decoder1
Dense Object Detection1

Usage over time archive 2025-07-28

Papers per year tagged with Cascade R-CNN: 2017 to 2025, peak 10 10 0 2017: 1 paper 2017 2018: 1 paper 2018 2019: 10 papers 2019 2020: 5 papers 2020 2021: 5 papers 2021 2022: 5 papers 2022 2023: 3 papers 2023 2024: 3 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (34 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Object Detection Models

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