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Found 10 papers, 3 papers with code
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.
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.
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.
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.
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)
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.
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-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM
Bayesian models provide a framework for probabilistic modelling of complex datasets.
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning.
Distributed Probabilistic Learning for Camera Networks with Missing Data
In this work we present an approach to estimation and learning of generative probabilistic models in a distributed context where certain sensor data can be missing.
