Comments (1)
First of all, I have to admit that CFNet is the first official publication (in CVPR2017) of an end-to-end learning framework about CF.
Compared to CFNet that is end-to-end pre-trained on the same training dataset, DCFNet achieves a relative gain of 9.8% in AUC because it extracts features without resolution loss, carries out CF based appearance modeling and tracking consistently in the frequency domain.
Without Resolution Loss
The feature extractor of our DCFNet never reduce resolution (stride = 1).
In simple terms, this may be a simple difference in network architecture design.
I think that this is a very important factor for visual tracking, and if there is no border effect, a DCF operatation on a dense features can be interpreted as a approximation of Continuous Convolution.
Besides, I have do a lot of experiment about network architecture and resolution. From our experiments, we observe that decreasing feature spatial resolution can cause a large reduction in the AUC accuracy. (33<63<125<169)
Consistently in the frequency domain
The CFNet is a improved version of SiamFC.
The filter of CFNet learned is croped to a small size (17x17) for time-domain correlation, which will strongly harm the performance.
So far, I have not seen the CFNet source code. I guess the main reason for the crop operation is to be consistent with SiamFC.
(Just Imagine) Even if the training image and test image are the same image, the cropped filter may produce a bad response. For a normal CF (not SRDCF), there's no guarantee that the center part of filter are more effective.
In general, CFNet is a very good paper with perfect proofs and experimental controls.
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Related Issues (20)
- cos window multiple feature map (training) HOT 2
- Does VID training use 86G data? HOT 2
- Python Equivalent HOT 1
- Impact of feature size on the tracking performance
- Is it possible to use stride in the network, how would it impact the tracking performance? HOT 2
- training process question HOT 1
- 关于训练后的模型评测
- Loading is very slow
- VOT评测指标
- the padding of the conv layers
- 我想在conv1之后插入一个BN层,虽然保存好模型但是不能用,出现一些错误,请问怎么插入,找了很久都没有找到这个BN的matconvnet的deploy 例子,谢谢
- How does imgcrop_multiscale.m work?
- About the training data VID2015 and training time
- What is the function of LRN layer?
- the different feature size in the training and tracking process
- I meet the same problem with the same environment. After I change the default setting to nonrecursive, the problem still exists. Although the hint change, the problem seems unchanged. HOT 1
- how to implement the pooling layer or other functions defined by ourselves?
- Compatibility with newer Pytorch version?
- Is it better to shift the gaussion label? HOT 4
- ERROR in DCFNet/DCFNet/run_DCFNet.m, line53? HOT 3
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