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Found 27 papers, 11 papers with code
Towards Objectively Benchmarking Social Intelligence for Language Agents at Action Level
To gauge the significance of agent architecture, we implement a target-driven planning (TDP) module as an adjunct to the existing agent.
AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents
In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives.
Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
In the end, we integrate our LCR-Net into a SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor driving environments.
UniWorld: Autonomous Driving Pre-training via World Models
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots.
Controlling Character Motions without Observable Driving Source
A high-level prior model can be easily injected on top to generate unlimited long and diverse sequences.
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
In this paper, we propose the first fully push-forward-based Distributional Reinforcement Learning algorithm, called Push-forward-based Actor-Critic EncourageR (PACER).
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction
When compared to monocular pre-training methods on the nuScenes dataset, UniScene shows a significant improvement of about 2. 0% in mAP and 2. 0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion.
RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences.
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained.
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds using masked autoencoders (MAE).
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning.
On the Value of Infinite Gradients in Variational Autoencoder Models
A number of recent studies of continuous variational autoencoder (VAE) models have noted, either directly or indirectly, the tendency of various parameter gradients to drift towards infinity during training.
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task
In this paper, we study how SSL can enhance the performance of the out-of-distribution (OOD) detection task.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Trajectory Prediction for Autonomous Driving with Topometric Map
The experimental results show that the proposed method outperforms state-of-the-art multimodal methods and is robust to the perturbations of the topometric map.
Attentional Graph Neural Network for Parking-slot Detection
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection.
Further Analysis of Outlier Detection with Deep Generative Models
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling.
Active Disturbance Rejection Control Design with Suppression of Sensor Noise Effects in Application to DC-DC Buck Power Converter
The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise.
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
3D moving object detection is one of the most critical tasks in dynamic scene analysis.
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions.
Lidar-based Object Classification with Explicit Occlusion Modeling
Object detection and classification based on lidar point cloud is a key technology for UGV.
Diagnosing and Enhancing VAE Models
Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood.
Compressing Neural Networks using the Variational Information Bottelneck
Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture.
Compressing Neural Networks using the Variational Information Bottleneck
Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture.
Understanding and Predicting The Attractiveness of Human Action Shot
Selecting attractive photos from a human action shot sequence is quite challenging, because of the subjective nature of the "attractiveness", which is mainly a combined factor of human pose in action and the background.
Hidden Talents of the Variational Autoencoder
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution.
Semi-supervised Gender Classification with Joint Textual and Social Modeling
In gender classification, labeled data is often limited while unlabeled data is ample.
Multivariate Bernoulli distribution
In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes.
