Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms

• URL: http://arxiv.org/abs/2108.09394v1
• Date: Fri, 20 Aug 2021 22:50:41 GMT
• Title: Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms
• Authors: Taeyeong Choi, Benjamin Pyenson, Juergen Liebig, Theodore P. Pavlic
• Abstract summary: We propose training deep network models to predict system-level states directly from generic graphical features from the entire view. Because the resulting predictive models are not based on human-understood predictors, we use explanatory modules. This represents an example of augmented intelligence in behavioral ecology -- knowledge co-creation in a human-AI team.
• Score: 3.441021278275805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most deep-learning frameworks for understanding biological swarms are designed to fit perceptive models of group behavior to individual-level data (e.g., spatial coordinates of identified features of individuals) that have been separately gathered from video observations. Despite considerable advances in automated tracking, these methods are still very expensive or unreliable when tracking large numbers of animals simultaneously. Moreover, this approach assumes that the human-chosen features include sufficient features to explain important patterns in collective behavior. To address these issues, we propose training deep network models to predict system-level states directly from generic graphical features from the entire view, which can be relatively inexpensive to gather in a completely automated fashion. Because the resulting predictive models are not based on human-understood predictors, we use explanatory modules (e.g., Grad-CAM) that combine information hidden in the latent variables of the deep-network model with the video data itself to communicate to a human observer which aspects of observed individual behaviors are most informative in predicting group behavior. This represents an example of augmented intelligence in behavioral ecology -- knowledge co-creation in a human-AI team. As proof of concept, we utilize a 20-day video recording of a colony of over 50 Harpegnathos saltator ants to showcase that, without any individual annotations provided, a trained model can generate an "importance map" across the video frames to highlight regions of important behaviors, such as dueling (which the AI has no a priori knowledge of), that play a role in the resolution of reproductive-hierarchy re-formation. Based on the empirical results, we also discuss the potential use and current challenges.

Related papers

• Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models [18.327960366321655]
  We develop a deep learning-based social cue integration model for saliency prediction to predict scanpaths in videos. We evaluate our approach on gaze of dynamic social scenes, observed under the free-viewing condition. Results indicate that a single unified model, trained on all the observers' scanpaths, performs on par or better than individually trained models.
  arXiv  Detail & Related papers  (2024-05-05T13:15:11Z)
• Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption [64.07607726562841]
  Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration. In this work, we tackle the task of reconstructing closely interactive humans from a monocular video. We propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information.
  arXiv  Detail & Related papers  (2024-04-17T11:55:45Z)
• Visual Affordance Prediction for Guiding Robot Exploration [56.17795036091848]
  We develop an approach for learning visual affordances for guiding robot exploration. We use a Transformer-based model to learn a conditional distribution in the latent embedding space of a VQ-VAE. We show how the trained affordance model can be used for guiding exploration by acting as a goal-sampling distribution, during visual goal-conditioned policy learning in robotic manipulation.
  arXiv  Detail & Related papers  (2023-05-28T17:53:09Z)
• Predicting the long-term collective behaviour of fish pairs with deep learning [52.83927369492564]
  This study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach to experiments and to the results of a state-of-the-art analytical model. We demonstrate that machine learning models social interactions can directly compete with their analytical counterparts in subtle experimental observables.
  arXiv  Detail & Related papers  (2023-02-14T05:25:03Z)
• Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions [82.90906153293585]
  We propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.
  arXiv  Detail & Related papers  (2022-06-25T09:55:39Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Model-agnostic Fits for Understanding Information Seeking Patterns in Humans [0.0]
  In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task. Here, we reexamine data from previous carefully designed experiments, collected at scale, that measured and catalogued these biases in aggregate form. We design deep learning models that replicate these biases in aggregate, while also capturing individual variation in behavior.
  arXiv  Detail & Related papers  (2020-12-09T04:34:58Z)
• Learning Human-Object Interaction Detection using Interaction Points [140.0200950601552]
  We propose a novel fully-convolutional approach that directly detects the interactions between human-object pairs. Our network predicts interaction points, which directly localize and classify the inter-action. Experiments are performed on two popular benchmarks: V-COCO and HICO-DET.
  arXiv  Detail & Related papers  (2020-03-31T08:42:06Z)
• Unsupervised Gaze Prediction in Egocentric Videos by Energy-based Surprise Modeling [6.294759639481189]
  Egocentric perception has grown rapidly with the advent of immersive computing devices. Human gaze prediction is an important problem in analyzing egocentric videos. We quantitatively analyze the generalization capabilities of supervised, deep learning models on the egocentric gaze prediction task.
  arXiv  Detail & Related papers  (2020-01-30T21:52:38Z)
• Deep learning reveals hidden interactions in complex systems [0.0]
  AgentNet is a model-free data-driven framework consisting of deep neural networks to reveal hidden interactions in complex systems. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds.
  arXiv  Detail & Related papers  (2020-01-03T02:25:50Z)

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.