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Compared to other classical object detectors, this work achieves very competitive result in terms of average precision (AP) and computational speed.
Experiments show that the mean average precision of object detection is improved after the addition of MSCA to the current object detection model.
In general, our method takes detection bounding boxes of a generic detector as input and generates the detection output with higher average precision and precise object regions.
Open accessProceedings ArticleDOI
14 Jun 2020
74 Citations
Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods 1.
The experimental results show that the proposed method improves the average precision of these two detection networks.
Proceedings ArticleDOI
Lingchao Kong, Rui Dai, Yuchi Zhang 
19 Aug 2016
15 Citations
Experimental results show that the model can achieve high accuracy in predicting the performance of object detection.

Related Questions

What is formula for average variance extracted?5 answersThe formula for average variance extracted (AVE) is a crucial indicator used to validate constructs. In statistics, AVE quantifies the variance captured by a construct relative to the measurement error. While standard deviation is commonly used in computations like correlation and regression, Truman L. Kelley's formula simplifies the calculation of average deviation, a measure of variability. Additionally, in meta-analysis, the average correlation estimator is favored over the observed correlation for sampling error variance, especially when dealing with a large number of studies and minimal variation in population correlations. Furthermore, for discretely-sampled variance swaps, a simplified analytical approach has been developed to price them using the realized variance, with a focus on ensuring the correct parameter subspace for accurate pricing.
How does mean average precision (MAP) compare to other metrics of ranking quality?5 answersMean Average Precision (MAP) is a widely used metric for evaluating the quality of object detectors and retrieval systems. However, it has some limitations. One limitation is that MAP evaluates detectors based on ranked instance retrieval, which may not be suitable for all downstream tasks. Another limitation is that MAP does not incorporate graded relevance, which is important in many information retrieval scenarios. To address these limitations, alternative metrics have been proposed. Graded Average Precision (GAP) is a measure that generalizes MAP to incorporate multi-graded relevance and has been shown to be informative and discriminative. Rank-Biased Precision (RBP) is another metric that assigns effectiveness scores to rankings based on geometrically weighted sums of document relevance values. Overall, while MAP is a dominant metric, alternative metrics like GAP and RBP offer different perspectives on ranking quality and can be valuable in specific evaluation scenarios.
What is mean average precision in model?5 answersMean Average Precision (mAP) is a standard score used to evaluate object detectors. It is a performance metric that measures the accuracy of object detection models. mAP takes into account both precision and recall, providing a comprehensive evaluation of the model's ability to detect objects in an image. It is widely used in academia and industry for comparing and benchmarking different object detection algorithms. mAP is sensitive to small perturbations in bounding box positions, with even a one-pixel shift causing a significant drop in mAP. However, mAP has been criticized for its inability to balance retrieval accuracy and utilization of hash codes in hashing-based retrieval systems. Anaphora resolution has been shown to improve the Mean Average Precision of retrieval systems, indicating the importance of resolving anaphoric references in information retrieval.
What is the formula for average precision metric for object detection?5 answersThe formula for average precision (AP) metric for object detection varies depending on the specific implementation and point interpolation used in the plot. Different variants of AP can be defined, resulting in different results. The lack of consensus in different works and implementations of AP is a problem faced by the academic and scientific communities. AP is widely used to evaluate detection accuracy of image and video object detectors. However, it has been pointed out that AP alone is not sufficient to capture the temporal nature of video object detection. To address this, alternative metrics such as average delay (AD) have been proposed to measure and compare detection delay in video object detection.
What is mean average precision in object detection?4 answersMean Average Precision (mAP) is a widely used metric in object detection to evaluate the accuracy of detection algorithms. It measures the average precision across different object categories. Traditional Precision-Recall curves used to calculate mAP often have a zigzag shape, making it difficult to compare algorithms. To address this, statistical approaches have been proposed to construct Precision-Recall curves. These statistical curves guarantee monotonically non-increasing behavior and calculate precision and recall based on statistical confidence levels. Another aspect to consider in video object detection is the temporal nature of the task. While mAP is commonly used, it is not sufficient to capture the temporal characteristics. To address this, a comprehensive metric called Average Delay (AD) has been proposed to measure and compare detection delay in video object detection.
Effect of variance to precision and accuracy?5 answersThe variance of a measurement can have an impact on both precision and accuracy. Precision refers to the consistency and reproducibility of measurements, while accuracy refers to how close the measurements are to the true value. A higher variance can lead to lower precision, as it indicates greater variability in the measurements. This means that repeated measurements may not consistently yield the same result. On the other hand, accuracy can be affected by both variance and bias. Variance alone does not necessarily affect accuracy, but it can contribute to uncertainty in the measurements. Therefore, it is important to consider both precision and accuracy when evaluating the impact of variance on measurement results.

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