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Found 19 papers, 6 papers with code
GSIR: Generalizable 3D Shape Interpretation and Reconstruction
Single image 3D shape interpretation and reconstruction are closely related to each other but have long been studied separately and often end up with priors that are highly biased by training classes.
Robot Parkour Learning
Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments.
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
However, these detectors usually require training on large amounts of annotated data that is expensive and time-consuming to collect.
Wanderlust: Online Continual Object Detection in the Real World
To bridge the gap, we present a new online continual object detection benchmark with an egocentric video dataset, Objects Around Krishna (OAK).
Molecular Contrastive Learning of Representations via Graph Neural Networks
In this work, we present MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework that leverages large unlabeled data (~10M unique molecules).
PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection
Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure.
CLOUD: Contrastive Learning of Unsupervised Dynamics
Developing agents that can perform complex control tasks from high dimensional observations such as pixels is challenging due to difficulties in learning dynamics efficiently.
SEMI: Self-supervised Exploration via Multisensory Incongruity
The variance of actions is further used to measure action incongruity.
Uncertainty-aware Self-supervised 3D Data Association
Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association.
AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics
Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods.
PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding
We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation.
Motion Prediction in Visual Object Tracking
Although our baseline system is a straightforward combination of standard methods, we obtain state-of-the-art results.
Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting
Through experiments on a robotic manipulation dataset and two driving datasets, we show that SPFNet is effective for the SPF task, our forecast-then-detect pipeline outperforms the detect-then-forecast approaches to which we compared, and that pose forecasting performance improves with the addition of unlabeled data.
AlignNet: A Unifying Approach to Audio-Visual Alignment
We present AlignNet, a model that synchronizes videos with reference audios under non-uniform and irregular misalignments.
Deep Mixture Density Network for Probabilistic Object Detection
The covariances help to learn the relationship between the borders, and the mixture components potentially learn different configurations of an occluded part.
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks
Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks.
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in the 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods.
Ranked #3 on
3D Multi-Object Tracking
on KITTI
Prediction-Tracking-Segmentation
We introduce a prediction driven method for visual tracking and segmentation in videos.
Bounding Box Regression with Uncertainty for Accurate Object Detection
Large-scale object detection datasets (e. g., MS-COCO) try to define the ground truth bounding boxes as clear as possible.
Ranked #21 on
Object Detection
on PASCAL VOC 2007
