Search Results for author: JiaWei He

Found 31 papers, 13 papers with code

Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detection

1 code implementation10 Jan 2024 Sicong Huang, JiaWei He, Kry Yik Chau Lui

Second, introducing new theoretic tools such as nearly essential support, essential distance and co-Lipschitzness, we obtain non-asymptotic provable OOD detection guarantees for certain distillation of the minimal sufficient statistics.

Out of Distribution (OOD) Detection Test

Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

1 code implementation29 Nov 2023 Yuqi Wang, JiaWei He, Lue Fan, Hongxin Li, Yuntao Chen, Zhaoxiang Zhang

In autonomous driving, predicting future events in advance and evaluating the foreseeable risks empowers autonomous vehicles to better plan their actions, enhancing safety and efficiency on the road.

Autonomous Driving

Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections

1 code implementation18 Sep 2023 Ting Meng, Chunyun Fu, Mingguang Huang, Xiyang Wang, JiaWei He, Tao Huang, Wankai Shi

However, in terms of the detection confidence fusing classification and localization, objects of low detection confidence may have inaccurate localization but clear appearance; similarly, objects of high detection confidence may have inaccurate localization or unclear appearance; yet these objects are not further classified.

Multi-Object Tracking

Tracking Objects with 3D Representation from Videos

no code implementations8 Jun 2023 JiaWei He, Lue Fan, Yuqi Wang, Yuntao Chen, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang

In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space.

Multiple Object Tracking Object +1

Weakly Supervised 3D Object Detection with Multi-Stage Generalization

no code implementations8 Jun 2023 JiaWei He, Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang

We devise the DoubleClustering algorithm to obtain object clusters from reconstructed scene-level points, and further enhance the model's detection capabilities by developing three stages of generalization: progressing from complete to partial, static to dynamic, and close to distant.

3D Reconstruction Monocular 3D Object Detection +3

Learnable Graph Matching: A Practical Paradigm for Data Association

1 code implementation27 Mar 2023 JiaWei He, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang

Data association is at the core of many computer vision tasks, e. g., multiple object tracking, image matching, and point cloud registration.

Graph Matching Multiple Object Tracking +1

3D Multi-Object Tracking Based on Uncertainty-Guided Data Association

1 code implementation3 Mar 2023 JiaWei He, Chunyun Fu, Xiyang Wang

In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage.

3D Multi-Object Tracking

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 +3

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 Object

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.

Position

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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