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Found 13 papers, 4 papers with code
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving
However, there is a lack of underground parking scenario datasets with multiple sensors and well-labeled images that support both SLAM tasks and perception tasks, such as semantic segmentation and parking slot detection.
MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion
To achieve so, we inject the semantic alignment into the radar features via the semantic-aligned radar encoder (SARE) to produce image-guided radar features.
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts
Monocular 3D object detection reveals an economical but challenging task in autonomous driving.
Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection
Specifically, we leverage intermediate features and responses for knowledge distillation.
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
We design a simple yet effective HopGNN framework that can easily utilize existing GNNs to achieve hop interaction.
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information.
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing
The performance of GNNs degrades as they become deeper due to the over-smoothing.
Ranked #5 on
Node Classification
on Squirrel
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Moreover, we propose a Conv-Agnostic GNNs framework (CAGNNs) to enhance the performance of GNNs on heterophily datasets by learning the neighbor effect for each node.
Memory-based Message Passing: Decoupling the Message for Propogation from Discrimination
Based on the homophily assumption, the current message passing always aggregates features of connected nodes, such as the graph Laplacian smoothing process.
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification
In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.
SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition
In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones.
Crowd Counting with Density Adaption Networks
Crowd counting is one of the core tasks in various surveillance applications.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.
