research
∙
09/18/2023
Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections
In currently available literature, no tracking-by-detection (TBD) paradi...
share
research
∙
06/08/2023
2D Supervised Monocular 3D Object Detection by Global-to-Local 3D Reconstruction
With the advent of the big model era, the demand for data has become mor...
research
∙
06/08/2023
Tracking Objects with 3D Representation from Videos
Data association is a knotty problem for 2D Multiple Object Tracking due...
research
∙
04/18/2023
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
Firstly, a new multi-object tracking framework is proposed in this paper...
research
∙
03/27/2023
3D Video Object Detection with Learnable Object-Centric Global Optimization
We explore long-term temporal visual correspondence-based optimization f...
research
∙
03/27/2023
Learnable Graph Matching: A Practical Paradigm for Data Association
Data association is at the core of many computer vision tasks, e.g., mul...
research
∙
03/03/2023
3D Multi-Object Tracking Based on Uncertainty-Guided Data Association
In the existing literature, most 3D multi-object tracking algorithms bas...
research
∙
07/20/2022
Densely Constrained Depth Estimator for Monocular 3D Object Detection
Estimating accurate 3D locations of objects from monocular images is a c...
research
∙
02/24/2022
DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association
In the recent literature, on the one hand, many 3D multi-object tracking...
research
∙
04/24/2021
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
Humans accumulate knowledge in a lifelong fashion. Modern deep neural ne...
research
∙
03/30/2021
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking
Data association across frames is at the core of Multiple Object Trackin...
research
∙
02/25/2021
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Learning from heterogeneous data poses challenges such as combining data...
research
∙
02/12/2021
Jacobian Determinant of Normalizing Flows
Normalizing flows learn a diffeomorphic mapping between the target and b...
research
∙
10/07/2020
Improving Sequential Latent Variable Models with Autoregressive Flows
We propose an approach for improving sequence modeling based on autoregr...
research
∙
03/27/2020
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference
Urban air pollution has become a major environmental problem that threat...
research
∙
10/18/2019
Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to cap...
research
∙
07/24/2019
LayoutVAE: Stochastic Scene Layout Generation from a Label Set
Recently there is an increasing interest in scene generation within the ...
research
∙
07/23/2019
Lifelong GAN: Continual Learning for Conditional Image Generation
Lifelong learning is challenging for deep neural networks due to their s...
research
∙
05/08/2019
Generative Model with Dynamic Linear Flow
Flow-based generative models are a family of exact log-likelihood models...
research
∙
04/05/2019
A Variational Auto-Encoder Model for Stochastic Point Processes
We propose a novel probabilistic generative model for action sequences. ...
research
∙
10/10/2018
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges ac...
research
∙
03/21/2018
Probabilistic Video Generation using Holistic Attribute Control
Videos express highly structured spatio-temporal patterns of visual data...
research
∙
05/30/2017