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Found 15 papers, 5 papers with code
Human Action Recognition Based on Spatial-Temporal Attention
Many state-of-the-art methods of recognizing human action are based on attention mechanism, which shows the importance of attention mechanism in action recognition.
SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field
Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems.
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models
Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models.
Contrastive Label Enhancement
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity.
HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration
Feature Descriptors and Detectors are two main components of feature-based point cloud registration.
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image
Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain.
3DMNDT:3D multi-view registration method based on the normal distributions transform
Under the NDT framework, this paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the K-means clustering and Lie algebra solver to achieve multi-view registration.
Effective multi-view registration of point sets based on student's t mixture model
What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers.
Multi-view Subspace Clustering Networks with Local and Global Graph Information
Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.
REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds
Reliable robotic grasping in unstructured environments is a crucial but challenging task.
Robotics
Visual Space Optimization for Zero-shot Learning
The other is to optimize the visual feature structure in an intermediate embedding space, and in this method we successfully devise a multilayer perceptron framework based algorithm that is able to learn the common intermediate embedding space and meanwhile to make the visual data structure more distinctive.
Constrained Bilinear Factorization Multi-view Subspace Clustering
Multi-view clustering is an important and fundamental problem.
A Real-time Robotic Grasp Approach with Oriented Anchor Box
The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors.
Robotics
ROI-based Robotic Grasp Detection for Object Overlapping Scenes
Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset.
Robotics
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
The feature extractor is a deep convolutional neural network.
