Hello Everyone, I am going to present our work.
Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis.
Human activity analysis is a core part in many user-centric applications, and it opens
up a vast potential to improve our daily life.
There has been extensive research on human activity recognition using different sensors
and different machine learning algorithms.
But, most previous works have some limitations.
For example, most of the previous works focused on learning from one aspect of the data.
Like: only recognizing the activity, or only identifying the person.
However, activity recognition and person identification are closely related, sharing knowledge between
related tasks leads to learning generalized representations, helping to reduce the risk
of overfitting, and therefore, combining them into one multi-task model is
reasonable and can be beneficial for both tasks.
Second, many person identification methods are based on iris, face, or fingerprint.
These methods require specific cooperation from the users.
It would be better if the person identification model can take different inputs to complete
the identification task.
Some other works identify person based on limited number of activities.
For example, gait recognition-based person identification.
It assumes walking is the only activity to be performed during the identification.
But, in real-world, this assumption may not hold.
In this work, we proposed a siamese networks based multi-task model.
The model will map the data into two representation spaces, one space for the activity, the other
space for the person.
In the activity representation space, the data will be grouped together, if they are
the same type of activity, or stay away from each other, if they are different activity
types.
Similarly, in the person representation space, if the data pairs are from the same person,
they will stay closely, but if the data pairs are from different person, they will stay
away from each other.
We visualize the representation vectors in the two representation spaces.
The first row is the activity representation space, the second row is the person representation
space.
As we can see that, each type of the activity is grouped into its own cluster in each representation
space.
We compare the results from out model with the results from other published works.
As we can see, the proposed multi-task method achieved competitive performance on both tasks
compared with the supervised single-task approaches in most situations.
In the second experiment, we expanded the proposed multi-task model to include additional
attribute representation learning.
Here, we choose the gender of the person as another representation learning task.
The experiment results show that three types of different representations are learned successfully.
So we know this model can be easily expanded to other related representation learning tasks.
In the third experiment, we consider that, in real world applications, it will be impossible
for the model to have complete and perfect information about the similarities between
all data items and for all attributes of interest.
Therefore, we divided the dataset into two parts of equal size.
One part of the data only provides the similarity information of the activity types, while the
other part of the data only provides the similarity information of the person identities.
As shown in the result table, with only partial similarity information, the performance of
the multi-task method is relatively close to the performance of the multi-task method
using the complete similarity information.
So it indicates that with partial similarity information, the model is capable of effectively
utilizing cross-task information to achieve performance that is competitive with other
models.
To further understand the effect of the multi-task learning framework, we conducted a series
of ablation experiments.
Each single-task model is trained to learn only one task.
We can see from the results that expanding the model from single-task learning to multi-task
learning does not cause performance loss in most cases.
On the contrary, the performance of multi-task model is most of the time better than the
single-task models.
Ok, This is a short presentation about the paper, thank you.