# Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark

arXiv: https://arxiv.org/abs/2305.18310 · HTML: https://mlsanity.com/paper/2305.18310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Temporal Relational Reasoning in Videos](https://mlsanity.com/paper/1711.08496/) (2017)
- [Forward and Reverse Gradient-Based Hyperparameter Optimization](https://mlsanity.com/paper/1703.01785/) (2017)
- [ECO: Efficient Convolutional Network for Online Video Understanding](https://mlsanity.com/paper/1804.09066/) (2018)
- [Temporal Interlacing Network](https://mlsanity.com/paper/2001.06499/) (2020)
- [A General Descent Aggregation Framework for Gradient-based Bi-level Optimization](https://mlsanity.com/paper/2102.07976/) (2021)
- [Augmenting Iterative Trajectory for Bilevel Optimization: Methodology, Analysis and Extensions](https://mlsanity.com/paper/2303.16397/) (2023)

## Abstract (arXiv)

Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding. However, there is still a lack of high-quality motion datasets for small bio-robotics, which presents more challenging scenarios for long-term movement prediction and behavior control based on third-person observation. In this study, we introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments based on predefined annotation rules. To enhance the robustness of motion prediction against these factors, we propose a Dual-stream Motion-Scenario Decoupling (textitDMSD) framework that effectively separates scenario-oriented and motion-oriented features and designs a scenario contrast loss and motion clustering loss for overall training. With such distinctive architecture, the dual-branch feature flow information is interacted and compensated in a decomposition-then-fusion manner. Moreover, we demonstrate significant performance improvements of the proposed textitDMSD framework on different difficulty-level tasks. We also implement long-term discretized trajectory prediction tasks to verify the generalization ability of the proposed dataset.