Comments (2)
Thank you for your inspiring work. However, I don't understand the motivation of FIDTM maps. In visualization, the FIDTM you proposed seems to be similar to the traditional Gaussian map if you set the kernel size small enough. What is the difference between these two maps? Have you compared the counting performance or localization performance of these two maps?
Using the small Gaussian kernel (e.g., kernel =2, 4) to generate the density map will exist overlap in the extremely dense region. Another way is directly to use the point map to represent the crowd, without overlap in the crowd, but the point map lacks supervised information (regressing a point map is difficult), causing undesirable counting and localization performance. The FIDT maps provide enough supervision information and without overlap in the dense region, having the advantage of both point map and density map. In our experiment, the localization performance of FIDT maps is significantly better than the density maps, while the counting result of both is similar.
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Thank you for your inspiring work. However, I don't understand the motivation of FIDTM maps. In visualization, the FIDTM you proposed seems to be similar to the traditional Gaussian map if you set the kernel size small enough. What is the difference between these two maps? Have you compared the counting performance or localization performance of these two maps?
Additionally, our recent work[1] has also proved that using small Gaussian kernels is not suitable for the crowd localization task, as shown in Tab12 in AutoScale[1].
[1] AutoScale: Learning to Scale for Crowd Counting and Localization. https://arxiv.org/pdf/1912.09632.pdf
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Related Issues (20)
- HRNet vs. VGG-16 HOT 1
- 视频测试出现问题 HOT 3
- 你好,代码似乎只用了L2 Loss没有用SSIM? HOT 2
- 1-SSIM 损失
- 请问XX_pred_fidt.txt这个预测位置的文档怎么生成,代码里好像没见有 HOT 1
- Has anyone deployed FIDTM in C++ HOT 2
- 请问可以提供下 nwpu 的generate代码吗 HOT 3
- Training on custom dataset HOT 2
- Huge or normal loss while training? HOT 2
- ISSIM-Loss HOT 1
- Queries on custom dataset HOT 2
- How to train on NWPU-Crowd dataset? Can you provide data preprocessing script? thank you HOT 1
- Real Time tracking / counting? HOT 6
- mat怎么生成?
- Testing & demo counts discrepancy HOT 1
- Testing issue HOT 2
- Is there a light weight version for crowd counting ? HOT 2
- Experimental result problem
- Question on JHU dataset
- Tracking not working as expected HOT 2
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