Reproduce on LRW1000,only get 38.6,how can get 41.4? about lipreading_using_temporal_convolutional_networks HOT 2 CLOSED

Hi,

The paper you mentioned made an improvement on LRW-1000. In that paper, I saw them used 40-frame sequence for training and testing. For each sequence, the targeted word is always located in the centre of the sequence.

In contrast, we segmented sequences given the annotations without any padding. The average duration for each sequence is 0.3 seconds (8 frames), which is less than 40 frames. Given both results, it indicates that the contextual information around the targeted word might be helpful.

Regarding the pre-processing, we followed "LRW-1000: A Naturally-Distributed Large-Scale Benchmarkfor Lip Reading in the Wild" to resize the cropped mouth ROIs to 122x122.

For training, we used batchsize=16 and learning rate(lr)=1.5e-4 not batchsize=32, lr=1e-3. We used Adam (weight_decay=1e-4) to train the model for 80 epochs and the learning rate is decayed by cosine scheduler without a warmup stage.

from lipreading_using_temporal_convolutional_networks.

could you please release the pretrain weights trained on LRW-1000 datasets?

from lipreading_using_temporal_convolutional_networks.