Mitigate the classification ambiguity via localization-classification sequence in object detection

Article Computer Science, Artificial Intelligence

FCOS: A Simple and Strong Anchor-Free Object Detector

Zhi Tian et al.

Summary: FCOS is a fully convolutional one-stage object detector that is anchor box free and achieves higher detection accuracy through post-processing non-maximum suppression.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Article Computer Science, Artificial Intelligence

Cascade R-CNN: High Quality Object Detection and Instance Segmentation

Zhaowei Cai et al.

Summary: In object detection, the commonly used IoU threshold of 0.5 can lead to noisy detections, and performance may degrade for larger thresholds. The Cascade R-CNN architecture addresses this issue by training detectors sequentially with increasing IoU thresholds and eliminating quality mismatches at inference, resulting in state-of-the-art performance and significant improvement in high-quality detection across various datasets. The model is also generalized to instance segmentation, achieving nontrivial improvements over existing methods.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence