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Temporal pooling. #9

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chandak opened this issue Mar 12, 2016 · 7 comments
Open

Temporal pooling. #9

chandak opened this issue Mar 12, 2016 · 7 comments

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@chandak
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chandak commented Mar 12, 2016

Hi Tomas, could you please guide me on how I should implement the cross-channel weighted sum-pooling (temporal, basically) layer since I could not find anything on that ?

@tpfister
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Hi,

Sure, happy to help. Sorry, I haven't had the bandwidth to clean up the temporal code for release.

Given a video you'd want to:

  1. Precompute dense optical flow
  2. Modify the data layer so it reads in multiple frames and the optical flow between them
  3. Add a warping layer which uses the optical flow to warp the heatmaps from the top spatial fusion layer (conv5_fusion in our prototxt)
  4. Add a single 1x1 convolution on top of the warping layer output with an L2 loss to the ground truth (so you now have 3 losses) & give it a higher weight (say loss_weight: 5)
  5. Train :)

We trained a single-frame network first (SpatialNet + Spatial Fusion Layers), initialised the above temporal network with its weights (plus gaussians for the added 1x1 convolution) & continued training.

Pull requests accepted if you or someone else has time to reimplement this in our public fork! :)

Hope that helps,
Tomas

@chandak
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chandak commented Apr 11, 2016

Yes, that helps a lot Tomas. Thanks a lot. Will keep in touch in case I
have any further queries.

Sincerely.
On Mon, Apr 11, 2016 at 6:22 AM, Tomas Pfister notifications@github.com
wrote:

Hi,

Sure, happy to help. Sorry, I haven't had the bandwidth to clean up the
temporal code for release.

Given a video you'd want to:

  1. Precompute dense optical flow
  2. Modify the data layer so it reads in multiple frames and the optical
    flow between them
  3. Add a warping layer which uses the optical flow to warp the heatmaps
    from the top spatial fusion layer (conv5_fusion in our prototxt)
  4. Add a single 1x1 convolution on top of the warping layer output with an
    L2 loss to the ground truth (so you now have 3 losses) & give it a higher
    weight (say loss_weight: 5)
  5. Train :)

We trained a single-frame network first (SpatialNet + Spatial Fusion
Layers), initialised the above temporal network with its weights (plus
gaussians for the added 1x1 convolution) & continued training.

Hope that helps!
Tomas


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Regards,
Chinmay Mahesh Chandak.
3rd year undergraduate,
Computer Science and Engineering,
IIT Hyderabad.

@zimenglan-sysu-512
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hi @tpfister, can you release the result of SpatialNet + Spatial Fusion Layers on flic dataset?

@jinyixin621
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Hi @tpfister how do you use the optical flow to warp 64x64 heatmaps? Thank you very much

@mindcont
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Hi, @jinyixin621
Have you found the answer yet?

Hi @tpfister how do you use the optical flow to warp 64x64 heatmaps? Thank you very much

I do not know how they warp optical flow file which is use deepflow2 precomputed from dataset to heatmaps like they said in paper ,have this part code released?
flowing-convnets

@jinyixin621
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Hi, @mindcont I didn't get reply from the author, but I implement one method by my own understanding.

@nitba
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nitba commented Oct 27, 2018

Hi @jinyixin621 , where can I find your implementation on warping heatmap layer?

Hi, @mindcont I didn't get reply from the author, but I implement one method by my own understanding.

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