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Found 15 papers, 5 papers with code
ModeSeq: Taming Sparse Multimodal Motion Prediction with Sequential Mode Modeling
Anticipating the multimodality of future events lays the foundation for safe autonomous driving.
A Reference-Based 3D Semantic-Aware Framework for Accurate Local Facial Attribute Editing
Facial attribute editing plays a crucial role in synthesizing realistic faces with specific characteristics while maintaining realistic appearances.
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction
Simulating realistic behaviors of traffic agents is pivotal for efficiently validating the safety of autonomous driving systems.
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.
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
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage.
Ranked #14 on
Object Detection
on COCO 2017 val
Multi-Stream Attention Learning for Monocular Vehicle Velocity and Inter-Vehicle Distance Estimation
Vehicle velocity and inter-vehicle distance estimation are essential for ADAS (Advanced driver-assistance systems) and autonomous vehicles.
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge.
S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality.
Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining
Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D datasets.
Unsupervised Disentanglement of Linear-Encoded Facial Semantics
We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision.
$S^3$: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality.
Expanding Sparse Guidance for Stereo Matching
The performance of image based stereo estimation suffers from lighting variations, repetitive patterns and homogeneous appearance.
Indoor Depth Completion with Boundary Consistency and Self-Attention
We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced.
Ranked #2 on
Depth Completion
on Matterport3D
Modeling Melodic Feature Dependency with Modularized Variational Auto-Encoder
Automatic melody generation has been a long-time aspiration for both AI researchers and musicians.
