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Found 14 papers, 6 papers with code
Probabilistic Modeling of Human Teams to Infer False Beliefs
We find that the players' behaviors are affected by what they see in their in-game field of view, their beliefs about the meaning of the markers, and their beliefs about which meaning the team decided to adopt.
Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG
However, the development of such a system is complicated by the chaotic nature of the atmosphere, and the consequent rapid changes that can occur in the structures of precipitation systems In this work, we develop two approaches (hereafter referred to as Nowcasting-Nets) that use Recurrent and Convolutional deep neural network structures to address the challenge of precipitation nowcasting.
Modular Procedural Generation for Voxel Maps
The benefits of this approach include rapid, scalable, and efficient development of virtual environments, the ability to control the statistics of the environment at a semantic level, and the ability to generate novel environments in response to player actions in real time.
Attentional Local Contrast Networks for Infrared Small Target Detection
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the domain knowledge.
Asymmetric Contextual Modulation for Infrared Small Target Detection
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset.
Attentional Feature Fusion
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures.
Ranked #651 on
Image Classification
on ImageNet
Attention as Activation
Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms.
Improving Visual Feature Extraction in Glacial Environments
We took a custom camera rig to Igloo Cave at Mt.
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal Models
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video
We provide a Bayesian generative model for the temporal scene that captures the joint probability of camera parameters, locations of people, their gaze, what they are looking at, and locations of visual attention.
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees
We propose a new general-purpose prior, a branching Gaussian processes (BGP), that models spatial smoothness and temporal dynamics of curves while enforcing attachment between them.
A Statistical Model for Recreational Trails in Aerial Images
We present a statistical model of aerial images of recreational trails, and a method to infer trail routes in such images.
Understanding Bayesian Rooms Using Composite 3D Object Models
For example, we model a chair as a set of four legs, a seat and a backrest.
Learning models of object structure
Model topologies are learned across groups of images, and one or more such topologies is linked to an object category (e. g. chairs).
