Comments (4)
We haven't tried it on the COCO dataset but we did successfully apply it to 2D animal trajectories. You can simply replace the input and try it yourself.
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OK. Thank you for your reply.
from predict-cluster.
@sukun1045 Hi! I'm wondering how to apply your method to animal trajectories. Could you give me some guidance on how to do this? Thanks!
from predict-cluster.
Hi, I believe you can perform similar training for animal trajectories. For example, you can prepare your animal trajectories data as a sequence with shape (time, Feature_dim) and train an encoder-decoder model with a weak decoder and simple reconstruction loss as described in the paper. Once you are done with the training, you can investigate the encoder's final state to see whether it contains any meaningful patterns.
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Related Issues (20)
- Clarification re. Fixed Weight (FW) implementation HOT 3
- eval of each person for ntu dataset HOT 3
- About UWA3DII dataset HOT 2
- UWA dataset HOT 2
- pytorch implementation HOT 8
- Pretrained model on NTU-CS HOT 1
- Rotation Matrix R HOT 2
- error with get_feature() when run the train.py HOT 2
- some question on KNN HOT 2
- what's the shape of data at every HOT 2
- UWA3D handling HOT 1
- Action to predict HOT 5
- UCLA Data HOT 2
- About encoder states trajectories visualization HOT 2
- About problem in running ucla_demo HOT 1
- Your method cannot be called unsupervised!
- Is something wrong in pytorch implementation HOT 4
- Dimension of the hidden layer HOT 4
- About NTU datasets HOT 34
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