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Found 20 papers, 6 papers with code
Conditional Causal Relationships between Emotions and Causes in Texts
The causal relationships between emotions and causes in text have recently received a lot of attention.
Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
To highlight the context in such special causal relationships, we propose a new task to determine whether or not an input pair of emotion and cause has a valid causal relationship under different contexts and extract the specific context clauses that participate in the causal relationship.
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit.
Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving
Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception.
QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction
For the first time, we show that a joint prediction model can outperform marginal prediction models even on the marginal metrics, which opens up new research opportunities in trajectory prediction.
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving
Due to the heterogeneity of the map data and trajectory data, many data-driven models for trajectory prediction and motion planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner.
Query-Centric Trajectory Prediction
A refinement module then takes the trajectory proposals as anchors and leverages anchor-based queries to refine the trajectories further.
Ranked #2 on
Motion Forecasting
on Argoverse CVPR 2020
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
Owing to its generalization, MixDA has penetrated a variety of applications which are also completely reviewed in this work.
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data
A generalized matrix-pencil approach is proposed for the estimation of complex exponential components with segmented signal samples, which is very efficient and provides super-resolution estimations.
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction
To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction.
Ranked #24 on
Motion Forecasting
on Argoverse CVPR 2020
A Generative Car-following Model Conditioned On Driving Styles
Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades.
Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems
In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method.
Prior-Guided Deep Interference Mitigation for FMCW Radars
The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals.
Jointly Attacking Graph Neural Network and its Explanations
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
In this paper, we investigate the impact of two primary types of adversarial attacks, perturbation attacks and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models.
Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition
In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated.
Matrix-Pencil Approach-Based Interference Mitigation for FMCW Radar Systems
A novel matrix pencil-based interference mitigation approach for FMCW radars is proposed in this paper.
CFAR-Based Interference Mitigation for FMCW Automotive Radar Systems
In this paper, constant false alarm rate (CFAR) detector-based approaches are proposed for interference mitigation of Frequency modulated continuous wave (FMCW) radars.
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction
Existing approaches address the task by first extracting emotion and cause clauses via two binary classifiers separately, and then training another binary classifier to pair them up.
Deep Adversarial Social Recommendation
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.
