Search Results for author:
Found 19 papers, 13 papers with code
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch
Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems.
DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation
In the second stage, a novel registration network is designed to extract pose-sensitive features and predict the representation of object partial point cloud in canonical space based on the deformation results from the first stage.
Temporally-Adaptive Models for Efficient Video Understanding
Spatial convolutions are extensively used in numerous deep video models.
Ranked #3 on
Action Recognition
on EPIC-KITCHENS-100
(using extra training data)
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes
In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes.
Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering
The proposed approach aims to improve the rendering realness by minimizing the spectrum discrepancy between real and synthesized images, especially on the high-frequency localized sharpness information which causes image blur visually.
BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling
The learning and aggregation of multi-scale features are essential in empowering neural networks to capture the fine-grained geometric details in the point cloud upsampling task.
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning
Previous methods achieved good performance on such manipulation tasks.
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories
Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
TAda! Temporally-Adaptive Convolutions for Video Understanding
This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos.
Ranked #67 on
Action Recognition
on Something-Something V2
(using extra training data)
AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition
Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation.
A Stronger Baseline for Ego-Centric Action Detection
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition
In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset.
Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation
Experiment results on two challenging datasets Cityscapes and COCO demonstrate that the RSP head performs competitively on both semantic segmentation and panoptic segmentation with high efficiency.
Point Cloud Completion by Learning Shape Priors
Then we learn a mapping to transfer the point features from partial points to that of the complete points by optimizing feature alignment losses.
Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation
We present a novel learning approach to recover the 6D poses and sizes of unseen object instances from an RGB-D image.
Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications
Our contributions are twofold: a) a novel dense connected prediction (DCP) layer is proposed to provide better object-level depth estimation and b) specifically for autonomous driving scenarios, dense geometrical constrains (DGC) is introduced so that precise scale factor can be recovered without additional cost for autonomous vehicles.
Ranked #54 on
Monocular Depth Estimation
on KITTI Eigen split
Cascaded Refinement Network for Point Cloud Completion
Point clouds are often sparse and incomplete.
Robust 6D Object Pose Estimation by Learning RGB-D Features
Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping.
