Search Results for author:
Found 8 papers, 5 papers with code
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems.
Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
We present Positional Diffusion, a plug-and-play graph formulation with Diffusion Probabilistic Models to address positional reasoning.
Leveraging commonsense for object localisation in partial scenes
With the proposed graph-based scene representation, we estimate the unknown position of the target object using a Graph Neural Network that implements a novel attentional message passing mechanism.
Under the Hood of Transformer Networks for Trajectory Forecasting
This paper proposes the first in-depth study of Transformer Networks (TF) and Bidirectional Transformers (BERT) for the forecasting of the individual motion of people, without bells and whistles.
Spatial Commonsense Graph for Object Localisation in Partial Scenes
The SCG is used to estimate the unknown position of the target object in two steps: first, we feed the SCG into a novel Proximity Prediction Network, a graph neural network that uses attention to perform distance prediction between the node representing the target object and the nodes representing the observed objects in the SCG; second, we propose a Localisation Module based on circular intersection to estimate the object position using all the predicted pairwise distances in order to be independent of any reference system.
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Our POMP method uses as input the current pose of an agent (e. g. a robot) and a RGB-D frame.
Transformer Networks for Trajectory Forecasting
In particular, the TF model without bells and whistles yields the best score on the largest and most challenging trajectory forecasting benchmark of TrajNet.
Ranked #12 on
Trajectory Prediction
on ETH/UCY
Understanding Deep Architectures by Visual Summaries
In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized.
