Search Results for author: JiaWei He

Found 21 papers, 6 papers with code

Densely Constrained Depth Estimator for Monocular 3D Object Detection

1 code implementation20 Jul 2022 Yingyan Li, Yuntao Chen, JiaWei He, Zhaoxiang Zhang

So these methods only use a small number of projection constraints and produce insufficient depth candidates, leading to inaccurate depth estimation.

Depth Estimation Graph Matching +2

DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association

1 code implementation24 Feb 2022 Xiyang Wang, Chunyun Fu, Zhankun Li, Ying Lai, JiaWei He

This association mechanism realizes tracking of an object in a 2D domain when the object is far away and only detected by the camera, and updating of the 2D trajectory with 3D information obtained when the object appears in the LiDAR field of view to achieve a smooth fusion of 2D and 3D trajectories.

3D Multi-Object Tracking

Agent Forecasting at Flexible Horizons using ODE Flows

no code implementations ICML Workshop INNF 2021 Alexander Radovic, JiaWei He, Janahan Ramanan, Marcus A Brubaker, Andreas Lehrmann

In this work we describe OMEN, a neural ODE based normalizing flow for the prediction of marginal distributions at flexible evaluation horizons, and apply it to agent position forecasting.

Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation

no code implementations ECCV 2020 Mengyao Zhai, Lei Chen, JiaWei He, Megha Nawhal, Frederick Tung, Greg Mori

In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks.

Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data

no code implementations25 Feb 2021 Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Thibaut Durand, Greg Mori

Learning from heterogeneous data poses challenges such as combining data from various sources and of different types.

Imputation

Jacobian Determinant of Normalizing Flows

no code implementations12 Feb 2021 Huadong Liao, JiaWei He

In this paper, we show that the Jacobian determinant mapping is unique for the given distributions, hence the likelihood objective of flows has a unique global optimum.

Improving Sequential Latent Variable Models with Autoregressive Flows

no code implementations7 Oct 2020 Joseph Marino, Lei Chen, JiaWei He, Stephan Mandt

We propose an approach for improving sequence modeling based on autoregressive normalizing flows.

Point Process Flows

no code implementations18 Oct 2019 Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, JiaWei He, Thibaut Durand, Marcus Brubaker, Greg Mori

Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.

Point Processes

Arbitrarily-conditioned Data Imputation

no code implementations pproximateinference AABI Symposium 2019 Micael Carvalho, Thibaut Durand, JiaWei He, Nazanin Mehrasa, Greg Mori

In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows.

Imputation

Variational Selective Autoencoder

no code implementations pproximateinference AABI Symposium 2019 Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori

Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.

Image Inpainting Imputation

Generative Model with Dynamic Linear Flow

1 code implementation8 May 2019 Huadong Liao, JiaWei He, Kunxian Shu

However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models.

Density Estimation

Variational Autoencoders with Jointly Optimized Latent Dependency Structure

no code implementations ICLR 2019 Jiawei He, Yu Gong, Joseph Marino, Greg Mori, Andreas Lehrmann

In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure.

SECaps: A Sequence Enhanced Capsule Model for Charge Prediction

no code implementations10 Oct 2018 Congqing He, Li Peng, Yuquan Le, JiaWei He, Xiangyu Zhu

In this paper, we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.

Generic Tubelet Proposals for Action Localization

no code implementations30 May 2017 Jiawei He, Mostafa S. Ibrahim, Zhiwei Deng, Greg Mori

Our class-independent TPN outperforms other tubelet generation methods, and our unified temporal deep network achieves state-of-the-art localization results on all three datasets.

Action Classification Action Localization +1

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