Random Dilation Networks for Action Recognition in Videos – Source Erogol.com
Lately, we (TwentyBN) took a part in Activity Net trimmed action recognition challenge. The dataset is called Kinetics and recently released. It is a collection of 10 second YouTube videos. Each video has a single label among 400 different action classes. The dataset released by DeepMind with a baseline 61% Top-1 and 81.3% Top-5. For baseline models please refer to their dataset paper. But, it took 2 months for people to briskly hoist the bar high above.
As you might see above, we have the best Top-5 accuracy with 97% which is ~16% improvement on top of the baseline. The average of Top-1 and Top-5 decides the leader-board which places us to 3rd place. Yet, it is a great result for us where we could dabble only 2 weeks with limited juice. Team matters here!! Thx to my mates Raghav Goyal and Valentin Haenel for being great.
Here, I like to succinctly describe our novel network architecture. It has the best single network performance. (We plan to share a more detailed description in a separate Medium soon.) Namely, it is called BesNet due to a cheap cryptographic reason :). BesNet yields 74% Top-1 with only RGB . It is half-size of the baseline network described in the DeepMind paper.
In detail, BesNet is devised on top of ResNet-50 architecture. Distinctly, BesNet performs 3D convolutions that are able to learn both spatiotemporal features. In a better extent, BesNet takes not a single frame, but a set of frames from a video. It convolves pixels between consecutive frames as wells as single frame pixels. Each ResNet-50 module buckled with 1×1 + 3×3 +1×1 filters in order. Each such module followed by a residual connection coming from preceding module. It uses ReLU activation followed by a Batch-Normalization for each layer. In order to convert Resnet-50 to BesNet, we inflate 1×1 filters to 3x1x1 filters and 3×3 filters to 1x3x3 filters where the ordering of the dimensions is sequence x height x width. After convolution layers, an average pooling layer aggregates spatial dimension as in the normal ResNet. Subsequently, a max pooling layer aggregates temporal dimensions. A fully-connected layer used for predictions.
BesNet is initialized with ImageNet weights. In order to convert 2D filter weights to 3D filter weights, we replicate 2D filters along an additional dimension and then normalize the weights by the replication factor. This normalization keeps the activation values stable despite the architectural change. For example, a 1×1 filter is converted to 3x1x1 by copying the 1×1 filter 3 times along the third dimension and weights are divided by 3 at the end.
In BesNet, 3x1x1 filters are responsible for temporal and spatial cross-channel regularities. 1x3x3 filters pay into only spatial properties of individual feature maps. This orientation excites several observations. First off, it decouples temporal and spatial computations. It learns specialized layers for each of the temporal and spatial dimensions. The idea also entertained by the pooling layers. We decomposed spatial and temporal dimension over average and max pooling layers respectively. BesNet reduces the spatial dimensions along the convolutional layers yet it keeps the size of temporal dimension constant. This makes BesNet flexible to handle videos with different number of frames. Hence, given a video with K frames, BesNet keeps the temporal dimension as K until the pooling layers. Thereafter, max pooling layer aggregates K temporal channels into one. In a practical sense, this is easy with a dynamic computational graph library. Pytorch is a bliss here !! (Sorry TF, You’re so crusty.)
BesNet has a peculiar use of dilation in 3x1x1 layers which defines the real novel aspect of our architecture. BesNet uses dilation only on temporal dimension and it picks a random dilation factor per 3x1x1 layer for each mini-batch. It sets padding parameters in accordance to keep the temporal dimension unchanged. At the test time, each layer computes outputs for each possible dilation factor, then takes the average of the output feature maps. Random dilation enables the network to learn complex temporal relations. It also regularizes the network in the temporal domain. In practice, it reduces the effect of FPS used for casting videos into frames.
We discuss that for Kinetics, it is important to learn long range relations between frames. Videos are long and they have only a single label. So the network needs to learn the general context of the video. In that sense, small motions that are observed by a normal 3D convolution are not that important. Random dilation pays into this. It augments the contextual temporal window of the network.
Our experiments with only frame futures support our hypothesis here. We extracted frame features with ResNet-50 and train an MLP after pooling the features. It gets 65% accuracy. It is better than DeepMind’s baseline network with 3D convolution layers. That shows us contextual information means more than motion learned by 3D layers.
Motion information might be complementary but not the core. It is then verified by the random dilation. BesNet with no dilation results 70% , dilation 2 68% and the random dilation 74% accuracy. This stands to be a simple empirical proof backing our claim here.
Random dilation is really easy to implement with Pytorch. Just take normal Conv class and overwrite its forward pass by randomizing dilation parameter. If you like to try out before we release fell free.
I try to give a very sketchy description of BesNet here by no means complete. Please ping me if you have any question. We plan to study BesNet a little more and share it in the near future in legit formats. We also plan to share a finer description of our challenge approach with some open-source enjoyment.
Please note that BesNet is a work in progress. Anyways, feedbacks are always warmly welcome. Best :).
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