Search Results for author:
Found 10 papers, 4 papers with code
Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures
In this paper, we propose an agent-based simulation tool, which is grounded in human cognition and decision-making, for evaluating and improving the effectiveness of building evacuation procedures and guidance systems during a disaster.
Deep Crowd-Flow Prediction in Built Environments
In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).
MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction
Accurate long-term trajectory prediction in complex scenes, where multiple agents (e. g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem.
HM: Hybrid Masking for Few-Shot Segmentation
A fundamental limitation of FM is the inability to preserve the fine-grained spatial details that affect the accuracy of segmentation mask, especially for small target objects.
Ranked #5 on
Few-Shot Semantic Segmentation
on FSS-1000 (1-shot)
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection and parameter tuning.
MSI: Maximize Support-Set Information for Few-Shot Segmentation
To extract information relevant to the target class, a dominant approach in best-performing FSS methods removes background features using a support mask.
Learning from Synthetic Human Group Activities
The study of complex human interactions and group activities has become a focal point in human-centric computer vision.
The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents
We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect.
Laying the Foundations of Deep Long-Term Crowd Flow Prediction
Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.
