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Found 12 papers, 7 papers with code
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.
OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task.
Multiscale Positive-Unlabeled Detection of AI-Generated Texts
Recent releases of Large Language Models (LLMs), e. g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts.
Pillar R-CNN for Point Cloud 3D Object Detection
The performance of point cloud 3D object detection hinges on effectively representing raw points, grid-based voxels or pillars.
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Local feature matching is an essential component in many visual applications.
An Electromagnetic-Information-Theory Based Model for Efficient Characterization of MIMO Systems in Complex Space
In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space.
DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching
In this work, we propose DeepMatcher, a deep Transformer-based network built upon our investigation of local feature matching in detector-free methods.
CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network
Finally, we propose a sign-based gradient surgery to promote the training of CO-Net, thereby emphasizing the usage of task-shared parameters and guaranteeing that each task can be thoroughly optimized.
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once
In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud with a straightforward yet effective multi-task network.
RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems.
PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection
Real-time and high-performance 3D object detection is of critical importance for autonomous driving.
Ranked #2 on
3D Object Detection
on nuScenes LiDAR only
Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
And this provides us a great opportunity to think about how shall these data be organized and exploited.
