Search Results for author:
Found 31 papers, 10 papers with code
DoTAT: A Domain-oriented Text Annotation Tool
Secondly, the tool provides annotation of events, nested event and nested entity, which are frequently required in domain-related text structuring tasks.
REPOFUSE: Repository-Level Code Completion with Fused Dual Context
The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase.
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models.
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages.
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization
FB merges the similarity come from MTCG and KAR to sort the terminologies from different perspectives.
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
The key idea is to decompose the 4D object label into two parts: the object size in 3D that's fixed through time for rigid objects, and the motion path describing the evolution of the object's pose through time.
PLUMENet: Efficient 3D Object Detection from Stereo Images
In this paper we propose a model that unifies these two tasks and performs them in the same metric space.
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
In this paper, we propose LaneRCNN, a graph-centric motion forecasting model.
Ranked #150 on
Motion Forecasting
on Argoverse CVPR 2020
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
Yet, there have been limited studies on the adversarial robustness of multi-modal models that fuse LiDAR features with image features.
Learning Joint 2D-3D Representations for Depth Completion
In this paper, we tackle the problem of depth completion from RGBD data.
Multi-Task Multi-Sensor Fusion for 3D Object Detection
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection.
Ranked #13 on
3D Object Detection
on KITTI Cars Easy
HDNET: Exploiting HD Maps for 3D Object Detection
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors.
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization.
Recovering and Simulating Pedestrians in the Wild
We then incorporate the reconstructed pedestrian assets bank in a realistic LiDAR simulation system by performing motion retargeting, and show that the simulated LiDAR data can be used to significantly reduce the amount of annotated real-world data required for visual perception tasks.
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
In this paper, we propose an end-to-end self-driving network featuring a sparse attention module that learns to automatically attend to important regions of the input.
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on
3D Object Detection
on OPV2V
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
We present a novel method for testing the safety of self-driving vehicles in simulation.
End-to-end Contextual Perception and Prediction with Interaction Transformer
We show that our approach can outperform the state-of-the-art on both datasets.
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity.
Learning Lane Graph Representations for Motion Forecasting
We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions.
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles.
FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets
This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN.
Physically Realizable Adversarial Examples for LiDAR Object Detection
Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations.
Identifying Unknown Instances for Autonomous Driving
In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning.
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks.
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
The model consists of two modules.
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling
We adopt a deep recurrent convolutional neural network (RCNN) for this task, which is originally proposed for object recognition.
Curvature Wavefront Sensing for the Large Synoptic Survey Telescope
The Large Synoptic Survey Telescope (LSST) will use an active optics system (AOS) to maintain alignment and surface figure on its three large mirrors.
Instrumentation and Methods for Astrophysics
Recurrent Convolutional Neural Network for Object Recognition
Inspired by this fact, we propose a recurrent CNN (RCNN) for object recognition by incorporating recurrent connections into each convolutional layer.
LSST: from Science Drivers to Reference Design and Anticipated Data Products
About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18, 000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27. 5$.
