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Found 6 papers, 4 papers with code
PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking
We view the unknown data association as a latent variable and apply Expectation Maximization (EM) to obtain a filter with update step in the same form as the Kalman filter but with expanded measurement vector of all potential associations.
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping
Meanwhile, we propose a method to predict numerous suction poses from an RGB-D image of a cluttered scene and demonstrate our superiority against several previous methods.
TDAF: Top-Down Attention Framework for Vision Tasks
Such spatial and attention features are nested deeply, therefore, the proposed framework works in a mixed top-down and bottom-up manner.
ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation
In this paper, we further improve spatio-temporal point cloud feature learning with a flexible module called ASAP considering both attention and structure information across frames, which we find as two important factors for successful segmentation in dynamic point clouds.
Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
Understanding sequential information is a fundamental task for artificial intelligence.
Deep RNN Framework for Visual Sequential Applications
There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along depth (spatial representation direction), making it easier to train when building deep by balancing these two directions; the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources.
