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Found 25 papers, 9 papers with code
ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving
Offboard perception aims to automatically generate high-quality 3D labels for autonomous driving (AD) scenes.
To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning
Reinforcement learning (RL) -- finding the optimal behaviour (also referred to as policy) maximizing the collected long-term cumulative reward -- is among the most influential approaches in machine learning with a large number of successful applications.
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
In addition, our user study shows that the vulnerability knowledge generated by Vul-RAG can serve as high-quality explanations which can improve the manual detection accuracy from 0. 60 to 0. 77.
Asphalt Concrete Characterization Using Digital Image Correlation: A Systematic Review of Best Practices, Applications, and Future Vision
This article presents a state-of-art review of DIC as a crucial tool for laboratory testing of asphalt concrete (AC), primarily focusing on the widely utilized 2D-DIC and 3D-DIC techniques.
Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach
This study investigates the affecting factors and develops a modified XCMP method to allow automatic thickness prediction of in-service asphalt pavement with non-uniform dielectric properties through depth.
When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach
Visual grounding refers to the process of associating natural language expressions with corresponding regions within an image.
Towards Knowledge-driven Autonomous Driving
This paper explores the emerging knowledge-driven autonomous driving technologies.
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving.
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.
Ranked #1 on
10-shot image generation
on FQL-Driving
AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer
Renal pathology, as the gold standard of kidney disease diagnosis, requires doctors to analyze a series of tissue slices stained by H&E staining and special staining like Masson, PASM, and PAS, respectively.
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods.
Ranked #1 on
3D Multi-Object Tracking
on Waymo Open Dataset
Sequential Knockoffs for Variable Selection in Reinforcement Learning
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge.
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81. 02 mAPH (L2) detection performance.
Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation
However, few current public datasets limit the potential exploration of deep learning in the application of pavement damage segmentation.
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods.
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision.
MOC-GAN: Mixing Objects and Captions to Generate Realistic Images
Correspondingly, a MOC-GAN is proposed to mix the inputs of two modalities to generate realistic images.
Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design
To tackle these issues, we propose a novel method based on conditional entropy in Bayesian theory to evaluate the sensor configurations containing both cameras and LiDARs.
CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes
For autonomous vehicles, an accurate calibration for LiDAR and camera is a prerequisite for multi-sensor perception systems.
Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging
While deep learning technologies are now capable of generating realistic images confusing humans, the research efforts are turning to the synthesis of images for more concrete and application-specific purposes.
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling.
Ranked #10 on
Speech Recognition
on LibriSpeech test-clean
Multistream CNN for Robust Acoustic Modeling
When combined with self-attentive SRU LM rescoring, multistream CNN contributes for ASAPP to achieve the best WER of 1. 75% on test-clean in LibriSpeech.
State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention With Dilated 1D Convolutions
Self-attention has been a huge success for many downstream tasks in NLP, which led to exploration of applying self-attention to speech problems as well.
Ranked #32 on
Speech Recognition
on LibriSpeech test-clean
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Therefore, to generate the images with preferred objects and rich interactions, we propose a semi-parametric method, PasteGAN, for generating the image from the scene graph and the image crops, where spatial arrangements of the objects and their pair-wise relationships are defined by the scene graph and the object appearances are determined by the given object crops.
Multi-task Learning for Financial Forecasting
Compared to the previous works, we use multiple networks to forecast multiple related stocks, using the shared and private information of them simultaneously through multi-task learning.
