Focal Loss For Dense Object Detection
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Focal Loss for Dense Object Detection
arxiv.orgFocal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Doll´ar Facebook AI Research (FAIR) well-classi ed examples CE(p t) = log(p t) FL(p t) = (1 p t) log(p t) Figure 1. We propose a novel loss we term the Focal Loss that adds a factor (1 Enabled by the focal loss, our simple one-stagep
Focal Loss for Dense Object Detection - CVF Open Access
openaccess.thecvf.com3. Focal Loss The Focal Loss is designed to address the one-stage ob-ject detection scenario in which there is an extreme im-balancebetween foregroundand backgroundclasses during training (e.g., 1:1000). We introduce the focal loss starting from the cross entropy (CE) loss for binary classification1: CE(p,y)= (−log(p) if y =1 −log(1−p ...
Generalized Intersection over Union: A Metric and A Loss ...
giou.stanford.edulem, the authors later introduce focal loss [13], which is orthogonal to the main focus of our paper. Most popular object detectors [20, 21, 3, 12, 13, 16] uti-lize some combination of the bounding box representations and losses mentioned above. These considerable efforts have yielded significant improvement in object detection.
Objects as Points
arxiv.orgObject detection powers many vision tasks like instance segmentation [7,21,32], pose estimation [3,15,39], track- ... dense supervised learning [39,60]. Inference is a single net- ... where and are hyper-parameters of the focal loss [33], and N is the number of keypoints in image I. The nor-
CornerNet: Detecting Objects as Paired Keypoints
openaccess.thecvf.comare the first to formulate the task of object detection as a task of detecting and grouping corners simultaneously. Another novelty of ours is the corner pooling layers that help better localize the corners. We also significantly modify the hourglass architecture and add our novel variant of focal loss [23] to help better train the network. 3 ...