Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?

• URL: http://arxiv.org/abs/2106.15321v1
• Date: Wed, 16 Jun 2021 17:39:35 GMT
• Title: Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?
• Authors: Laurent Boucaud, Daniel Aloise and Nicolas Saunier
• Abstract summary: We consider the problem of predicting the future path of a pedestrian using its motion history and the motion history of the surrounding pedestrians. Deep-learning has become the main tool used to model the impact of social interactions on a pedestrian's motion.
• Score: 2.180763067449862
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We consider the problem of predicting the future path of a pedestrian using its motion history and the motion history of the surrounding pedestrians, called social information. Since the seminal paper on Social-LSTM, deep-learning has become the main tool used to model the impact of social interactions on a pedestrian's motion. The demonstration that these models can learn social interactions relies on an ablative study of these models. The models are compared with and without their social interactions module on two standard metrics, the Average Displacement Error and Final Displacement Error. Yet, these complex models were recently outperformed by a simple constant-velocity approach. This questions if they actually allow to model social interactions as well as the validity of the proof. In this paper, we focus on the deep-learning models with a soft-attention mechanism for social interaction modeling and study whether they use social information at prediction time. We conduct two experiments across four state-of-the-art approaches on the ETH and UCY datasets, which were also used in previous work. First, the models are trained by replacing the social information with random noise and compared to model trained with actual social information. Second, we use a gating mechanism along with a $L_0$ penalty, allowing models to shut down their inner components. The models consistently learn to prune their soft-attention mechanism. For both experiments, neither the course of the convergence nor the prediction performance were altered. This demonstrates that the soft-attention mechanism and therefore the social information are ignored by the models.

Related papers

• The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models [78.69526166193236]
  Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases. We propose sc Social Bias Neurons to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost.
  arXiv  Detail & Related papers  (2024-06-14T15:41:06Z)
• Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior [0.11086185608421924]
  The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. We show that (i) social media use is predictable at the within and between-person level and that (ii) there are robust individual differences in the predictability of social media use.
  arXiv  Detail & Related papers  (2024-04-20T16:36:28Z)
• Understanding the Effect of Model Compression on Social Bias in Large Language Models [12.289003145872481]
  Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text. We study the impact of model compression via quantization and knowledge distillation on measures of social bias in LLMs.
  arXiv  Detail & Related papers  (2023-12-09T20:04:20Z)
• SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction [59.064925464991056]
  We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-05T01:49:18Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• Are socially-aware trajectory prediction models really socially-aware? [75.36961426916639]
  We introduce a socially-attended attack to assess the social understanding of prediction models. An attack is a small yet carefully-crafted perturbations to fail predictors. We show that our attack can be employed to increase the social understanding of state-of-the-art models.
  arXiv  Detail & Related papers  (2021-08-24T17:59:09Z)
• SRA-LSTM: Social Relationship Attention LSTM for Human Trajectory Prediction [3.1703939581903864]
  Social relationship among pedestrians is a key factor influencing pedestrian walking patterns. Social Relationship Attention LSTM (SRA-LSTM) model to predict future trajectories.
  arXiv  Detail & Related papers  (2021-03-31T12:56:39Z)
• PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception [50.551003004553806]
  We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions. Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
  arXiv  Detail & Related papers  (2021-03-02T18:44:57Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)
• Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction [26.28051910420762]
  Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN) modeled pedestrian interactions as a graph. Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with up to 48 times faster inference speed. We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix.
  arXiv  Detail & Related papers  (2020-02-27T05:40:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.