2D Tricube distribution problem

[aiboys]

Hi, Thanks for your open source. It is a good work. But I have the following problems: 1）2d tricube distribution is defined as (1-|x|^3)^r * (1-|y|^3)^r, such distribution is invariant with object's shape (aspect-ratio between height and width). But shown in Fig 7, the output heatmaps are deformable with the shape、aspect ratio and rotation degree. So I amd not sure that there is a modification in your 2d tricube distribution, right?

2) since segmentation based algorithms need post-processing, how about the time-consumption of your TricubeNet. I did not see the comparison experiments with other SOTA algorithms.

[qjadud1994]

Thank you for your interest in our work.

1. Actually, we pre-define the square 2D Tricube kernel as in Figure 3 (a) and reshape the kernel according to the shape of an object as in Figure 3 (c). You can notice this implementation in here.

2. In our pre-print version, our work measured the latency using another backbone network (but not included in the paper). With a light version of TricubeNet, which excludes the MAC and Cascade Refinement, our latency is as follows. (Dataset: HRSC2016, GPU: RTX 2080 Ti)

Method	Backbone	Input Size	mAP	FPS
R2 CNN	ResNet-101	800x800	73.1	2
RRPN	ResNet-101	800x800	79.1	4
RetinaNet-H	ResNet-101	800x800	82.9	14
RoI Trans	ResNet-101	800x800	86.2	6
RetinaNet-R	ResNet-101	800x800	89.2	10
R3Det	ResNet-101	800x800	89.3	12
TricubeNet	ResNet-101	512x512	87.7	46
TricubeNet (+TTA)	ResNet-101	512x512	88.6	17
TricubeNet	DLA-34	512x512	87.1	70
TricubeNet (+TTA)	DLA-34	512x512	88.4	30

As you can see, the whole inference is very fast with a light version, and there is no bottleneck in the segmentation-based post-processing algorithm.