Comments (6)
@liu666666 Hi, thanks for your suggestion. Yes, I actually already used the cross modality pre-training technique. Right now, my resnet101 and resnet152 flow model works good. It is just vgg16 model doesn't yield same performance. So it should be something particular about the VGG network.
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@bryanyzhu Hi, have you take the direction of optical flow into consideration? When flipping optical flow image, one should also invert the corresponding pixel value in x(horizontal)/y(vertical) channel, otherwise the direction will stay the same, which is contrast to its real direction.
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Hi @zed0630 Thanks for pointing out the mistake, I should do another invert. Will update the code base here. Thanks.
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@zed0630 Hi, I didn't get your point about inverting the pixel values in the x/y channels during flipping. Can you elaborate on this?
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@swathikirans because when you flip your images, e.g, horizontal flip, the flow at x direction also changes.
You can refer to this code snippet, i think it will help.
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@BryanZhu. Thank you!
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Related Issues (20)
- About pre-trained Model HOT 2
- test video HOT 5
- Question about training the models together HOT 2
- Different running env? HOT 5
- Can you provide your results for loss and accuracy values of spatial and temporal training?
- The number of GPUs? HOT 1
- I use your restnet152 model parameters for testing, but in split_1 the accuracy is only 67.59%.
- Use the video input from the camera for action recognition HOT 7
- Problems about VideoSpatialPrediction.py HOT 2
- How is the two streams fused ? HOT 1
- What is the accuracy of UCF101?
- what's version of pytorch and cuda
- dense_flow 可不可以在windows安装 HOT 1
- 如果没有安装dense_flow,运行build_of.py文件,是不是不会运行出结果 HOT 1
- fusion two stream feature?
- a PROBLEM when using VGG as motion model
- 老师我想问下怎么late fusion呀 HOT 1
- 关于抽帧的图片存放路径 HOT 2
- video sampling rate in training two-stream network
- About parameter --new_length in training RGB videos
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