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Found 8 papers, 2 papers with code
Object Dynamics Modeling with Hierarchical Point Cloud-based Representations
Modeling object dynamics with a neural network is an important problem with numerous applications.
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments.
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation
On the related problem of video instance segmentation, our method shows competitive performance with the previous best algorithm that requires joint training with the VOS algorithm.
Space Time Recurrent Memory Network
We propose a novel visual memory network architecture for the learning and inference problem in the spatial-temporal domain.
Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
Many approaches model each target in isolation and lack the ability to use all the targets in the scene to jointly update the memory.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
Multi-object Tracking with Neural Gating Using Bilinear LSTM
We also propose novel data augmentation approaches to efficiently train recurrent models that score object tracks on both appearance and motion.
Multiple Hypothesis Tracking Revisited
This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework.
