# Siamese Networks for Weakly Supervised Human Activity Recognition

arXiv: https://arxiv.org/abs/2307.08944 · HTML: https://mlsanity.com/paper/2307.08944/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention](https://mlsanity.com/paper/1810.04038/) (2018)
- [Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video](https://mlsanity.com/paper/1506.01911/) (2015)
- [Deep Activity Recognition Models with Triaxial Accelerometers](https://mlsanity.com/paper/1511.04664/) (2015)

## Built on by (most cited first, 4 of 4)

- [Consistency Based Weakly Self-Supervised Learning for Human Activity Recognition with Wearables](https://mlsanity.com/paper/2408.07282/) (2024)
- [Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches](https://mlsanity.com/paper/2512.19713/) (2025)
- [Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition](https://mlsanity.com/paper/2602.02542/) (2026)
- [Towards Generalizing Temporal Action Segmentation to Unseen Views](https://mlsanity.com/paper/2504.02512/) (2025)

## Abstract (arXiv)

Deep learning has been successfully applied to human activity recognition. However, training deep neural networks requires explicitly labeled data which is difficult to acquire. In this paper, we present a model with multiple siamese networks that are trained by using only the information about the similarity between pairs of data samples without knowing the explicit labels. The trained model maps the activity data samples into fixed size representation vectors such that the distance between the vectors in the representation space approximates the similarity of the data samples in the input space. Thus, the trained model can work as a metric for a wide range of different clustering algorithms. The training process minimizes a similarity loss function that forces the distance metric to be small for pairs of samples from the same kind of activity, and large for pairs of samples from different kinds of activities. We evaluate the model on three datasets to verify its effectiveness in segmentation and recognition of continuous human activity sequences.