Prioritized Sampling

Adaptive Training Sample Selection, or ATSS, is a method to automatically select positive and negative samples according to statistical characteristics of object. It bridges the gap between anchor-based and anchor-free detectors.

For each ground-truth box $g$ on the image, we first find out its candidate positive samples. As described in Line $3$ to $6$, on each pyramid level, we select $k$ anchor boxes whose center are closest to the center of $g$ based on L2 distance. Supposing there are $\mathcal{L}$ feature pyramid levels, the ground-truth box $g$ will have $k\times\mathcal{L}$ candidate positive samples. After that, we compute the IoU between these candidates and the ground-truth $g$ as $\mathcal{D}_g$ in Line $7$, whose mean and standard deviation are computed as $m_g$ and $v_g$ in Line $8$ and Line $9$. With these statistics, the IoU threshold for this ground-truth $g$ is obtained as $t_g=m_g+v_g$ in Line $10$. Finally, we select these candidates whose IoU are greater than or equal to the threshold $t_g$ as final positive samples in Line $11$ to $15$.

Notably ATSS also limits the positive samples' center to the ground-truth box as shown in Line $12$. Besides, if an anchor box is assigned to multiple ground-truth boxes, the one with the highest IoU will be selected. The rest are negative samples.

Source: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Object Detection 12 31.58%
Object 6 15.79%
Instance Segmentation 3 7.89%
Semantic Segmentation 3 7.89%
Real-time Instance Segmentation 2 5.26%
Decoder 2 5.26%
Dense Object Detection 2 5.26%
Quantization 1 2.63%
Classification 1 2.63%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories