DIDER: Discovering Interpretable Dynamically Evolving Relations

• URL: http://arxiv.org/abs/2208.10592v1
• Date: Mon, 22 Aug 2022 20:55:56 GMT
• Title: DIDER: Discovering Interpretable Dynamically Evolving Relations
• Authors: Enna Sachdeva, Chiho Choi
• Abstract summary: This paper introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a generic end-to-end interaction modeling framework with intrinsic interpretability. We evaluate DIDER on both synthetic and real-world datasets.
• Score: 14.69985920418015
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Effective understanding of dynamically evolving multiagent interactions is crucial to capturing the underlying behavior of agents in social systems. It is usually challenging to observe these interactions directly, and therefore modeling the latent interactions is essential for realizing the complex behaviors. Recent work on Dynamic Neural Relational Inference (DNRI) captures explicit inter-agent interactions at every step. However, prediction at every step results in noisy interactions and lacks intrinsic interpretability without post-hoc inspection. Moreover, it requires access to ground truth annotations to analyze the predicted interactions, which are hard to obtain. This paper introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a generic end-to-end interaction modeling framework with intrinsic interpretability. DIDER discovers an interpretable sequence of inter-agent interactions by disentangling the task of latent interaction prediction into sub-interaction prediction and duration estimation. By imposing the consistency of a sub-interaction type over an extended time duration, the proposed framework achieves intrinsic interpretability without requiring any post-hoc inspection. We evaluate DIDER on both synthetic and real-world datasets. The experimental results demonstrate that modeling disentangled and interpretable dynamic relations improves performance on trajectory forecasting tasks.

Related papers

• PEAR: Phrase-Based Hand-Object Interaction Anticipation [20.53329698350243]
  First-person hand-object interaction anticipation aims to predict the interaction process based on current scenes and prompts. Existing research typically anticipates only interaction intention while neglecting manipulation. We propose a novel model, PEAR, which jointly anticipates interaction intention and manipulation.
  arXiv  Detail & Related papers  (2024-07-31T10:28:49Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Disentangled Neural Relational Inference for Interpretable Motion Prediction [38.40799770648501]
  We develop a variational auto-encoder framework that integrates graph-based representations and timesequence models. Our model infers dynamic interaction graphs augmented with interpretable edge features that characterize the interactions. We validate our approach through extensive experiments on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-01-07T22:49:24Z)
• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network [0.0]
  We propose the attentive relational inference network (RAIN) to infer continuously weighted interaction graphs without any ground-truth interaction strengths. We show that our RAIN model with the PA mechanism accurately infers continuous interaction strengths for simulated physical systems in an unsupervised manner.
  arXiv  Detail & Related papers  (2022-08-28T09:13:33Z)
• VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction [50.986371459817256]
  We propose a novel textitVirtual InteRacTion mechanism, termed as VIRT, to enable full and deep interaction modeling in representation-based models. VIRT asks representation-based encoders to conduct virtual interactions to mimic the behaviors as interaction-based models do.
  arXiv  Detail & Related papers  (2021-12-08T09:49:28Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)
• RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection [40.65483058890176]
  Latest end-to-end HOI detectors are short of relation reasoning, which leads to inability to learn HOI-specific interactive semantics for predictions. We first present a progressive Relation-aware Frame, which brings a new structure and parameter sharing pattern for interaction inference. Based on modules above, we construct an end-to-end trainable framework named Relation Reasoning Network (abbr. RR-Net)
  arXiv  Detail & Related papers  (2021-04-30T14:03:10Z)
• Towards Interaction Detection Using Topological Analysis on Neural Networks [55.74562391439507]
  In neural networks, any interacting features must follow a strongly weighted connection to common hidden units. We propose a new measure for quantifying interaction strength, based upon the well-received theory of persistent homology. A Persistence Interaction detection(PID) algorithm is developed to efficiently detect interactions.
  arXiv  Detail & Related papers  (2020-10-25T02:15:24Z)
• EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning [41.42230144157259]
  We propose a generic trajectory forecasting framework with explicit relational structure recognition and prediction via latent interaction graphs. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. We introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance.
  arXiv  Detail & Related papers  (2020-03-31T02:49:23Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.