Search Results for author: Jiachen Li

Found 71 papers, 23 papers with code

CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts

1 code implementation9 May 2024 Jiachen Li, Xinyao Wang, Sijie Zhu, Chia-Wen Kuo, Lu Xu, Fan Chen, Jitesh Jain, Humphrey Shi, Longyin Wen

Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on multimodal tasks.

 Ranked #1 on Visual Question Answering on MMBench (GPT-3.5 score metric)

Image Captioning visual instruction following +1

CMP: Cooperative Motion Prediction with Multi-Agent Communication

no code implementations26 Mar 2024 Zhuoyuan Wu, Yuping Wang, Hengbo Ma, Zhaowei Li, Hang Qiu, Jiachen Li

Building on top of cooperative perception, this paper explores the feasibility and effectiveness of cooperative motion prediction.

Autonomous Vehicles motion prediction

Reward Guided Latent Consistency Distillation

no code implementations16 Mar 2024 Jiachen Li, Weixi Feng, Wenhu Chen, William Yang Wang

By distilling a latent consistency model (LCM) from a pre-trained teacher latent diffusion model (LDM), LCD facilitates the generation of high-fidelity images within merely 2 to 4 inference steps.

Image Generation

MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts

no code implementations9 Mar 2024 Zhuo Xu, Rui Zhou, Yida Yin, Huidong Gao, Masayoshi Tomizuka, Jiachen Li

Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications.

Data Augmentation Motion Planning

ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games

no code implementations28 Feb 2024 Shiqi Lei, Kanghoon Lee, Linjing Li, Jinkyoo Park, Jiachen Li

Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly.

Imitation Learning

Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation

no code implementations22 Jan 2024 Jiachen Li, Chuanbo Hua, Hengbo Ma, Jinkyoo Park, Victoria Dax, Mykel J. Kochenderfer

In this paper, we propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.

Relational Reasoning Robot Navigation +2

Graph Q-Learning for Combinatorial Optimization

no code implementations11 Jan 2024 Victoria M. Dax, Jiachen Li, Kevin Leahy, Mykel J. Kochenderfer

Graph-structured data is ubiquitous throughout natural and social sciences, and Graph Neural Networks (GNNs) have recently been shown to be effective at solving prediction and inference problems on graph data.

Combinatorial Optimization Decision Making +1

Disentangled Neural Relational Inference for Interpretable Motion Prediction

no code implementations7 Jan 2024 Victoria M. Dax, Jiachen Li, Enna Sachdeva, Nakul Agarwal, Mykel J. Kochenderfer

The results show superior performance compared to existing methods in modeling spatio-temporal relations, motion prediction, and identifying time-invariant latent features.

Motion Planning motion prediction

Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation

no code implementations27 Nov 2023 Jiachen Li, David Isele, Kanghoon Lee, Jinkyoo Park, Kikuo Fujimura, Mykel J. Kochenderfer

Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents.

Autonomous Navigation counterfactual +4

More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering

no code implementations16 Nov 2023 Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang

While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM's performance?

In-Context Learning Prompt Engineering

Video Instance Matting

1 code implementation7 Nov 2023 Jiachen Li, Roberto Henschel, Vidit Goel, Marianna Ohanyan, Shant Navasardyan, Humphrey Shi

To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.

Binarization Image Matting +4

Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification

1 code implementation26 Oct 2023 Jiachen Li, Xiaojin Gong

Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the necessity of prompt learning remain unclear due to the absence of semantic labels in ReID tasks.

Contrastive Learning Unsupervised Person Re-Identification +1

Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments

1 code implementation25 Sep 2023 Bernard Lange, Jiachen Li, Mykel J. Kochenderfer

We introduce the Scene Informer, a unified approach for predicting both observed agent trajectories and inferring occlusions in a partially observable setting.

Autonomous Vehicles Trajectory Prediction

For A More Comprehensive Evaluation of 6DoF Object Pose Tracking

no code implementations14 Sep 2023 Yang Li, Fan Zhong, Xin Wang, Shuangbing Song, Jiachen Li, Xueying Qin, Changhe Tu

The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.

Pose Tracking

Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning

1 code implementation12 Sep 2023 Enna Sachdeva, Nakul Agarwal, Suhas Chundi, Sean Roelofs, Jiachen Li, Mykel Kochenderfer, Chiho Choi, Behzad Dariush

The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.

Autonomous Vehicles Question Answering +2

Robust Driving Policy Learning with Guided Meta Reinforcement Learning

no code implementations19 Jul 2023 Kanghoon Lee, Jiachen Li, David Isele, Jinkyoo Park, Kikuo Fujimura, Mykel J. Kochenderfer

Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environment.

Autonomous Navigation Meta Reinforcement Learning +1

Matting Anything

1 code implementation8 Jun 2023 Jiachen Li, Jitesh Jain, Humphrey Shi

In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance.

Image Matting Referring Image Matting

Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints

no code implementations1 Jun 2023 Jiachen Li, Xinwei Shi, Feiyu Chen, Jonathan Stroud, Zhishuai Zhang, Tian Lan, Junhua Mao, Jeonhyung Kang, Khaled S. Refaat, Weilong Yang, Eugene Ie, CongCong Li

Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.

Action Recognition Autonomous Vehicles +3

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

1 code implementation11 Mar 2023 Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

Offline Reinforcement Learning with Closed-Form Policy Improvement Operators

no code implementations29 Nov 2022 Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang

Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning.

D4RL Offline RL +2

OneFormer: One Transformer to Rule Universal Image Segmentation

2 code implementations CVPR 2023 Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi

However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.

Instance Segmentation Panoptic Segmentation +3

Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments

no code implementations27 Sep 2022 Maneekwan Toyungyernsub, Esen Yel, Jiachen Li, Mykel J. Kochenderfer

Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions.

Autonomous Vehicles Segmentation +1

VMFormer: End-to-End Video Matting with Transformer

1 code implementation26 Aug 2022 Jiachen Li, Vidit Goel, Marianna Ohanyan, Shant Navasardyan, Yunchao Wei, Humphrey Shi

In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.

Decoder Video Matting

EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction

no code implementations10 Aug 2022 Jiachen Li, Chuanbo Hua, Jinkyoo Park, Hengbo Ma, Victoria Dax, Mykel J. Kochenderfer

While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited.

Relation Relational Reasoning +1

Causal Balancing for Domain Generalization

1 code implementation10 Jun 2022 Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations.

Domain Generalization

Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression

no code implementations24 May 2022 Jiachen Li, Ye Yuan, Hong-Bin Shen

Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data.

regression Symbolic Regression

Neighborhood Attention Transformer

5 code implementations CVPR 2023 Ali Hassani, Steven Walton, Jiachen Li, Shen Li, Humphrey Shi

We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.

Image Classification Object Detection +1

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

no code implementations CVPR 2022 Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin

Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.

3D Object Tracking Object +2

Important Object Identification with Semi-Supervised Learning for Autonomous Driving

no code implementations5 Mar 2022 Jiachen Li, Haiming Gang, Hengbo Ma, Masayoshi Tomizuka, Chiho Choi

We propose a novel approach for important object identification in egocentric driving scenarios with relational reasoning on the objects in the scene.

Autonomous Driving Binary Classification +5

ConvNeXt-backbone HoVerNet for nuclei segmentation and classification

1 code implementation28 Feb 2022 Jiachen Li, Chixin Wang, Banban Huang, Zekun Zhou

This manuscript gives a brief description of the algorithm used to participate in CoNIC Challenge 2022.

Classification Semantic Segmentation +1

Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification

1 code implementation15 Jan 2022 Menglin Wang, Jiachen Li, Baisheng Lai, Xiaojin Gong, Xian-Sheng Hua

Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results.

Clustering Contrastive Learning +1

Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation

no code implementations CVPR 2022 Hengbo Ma, Jiachen Li, Ramtin Hosseini, Masayoshi Tomizuka, Chiho Choi

Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.

Autonomous Driving Human motion prediction +3

SeMask: Semantically Masked Transformers for Semantic Segmentation

1 code implementation arXiv 2021 Jitesh Jain, Anukriti Singh, Nikita Orlov, Zilong Huang, Jiachen Li, Steven Walton, Humphrey Shi

To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation.

Decoder Semantic Segmentation

SS-MAIL: Self-Supervised Multi-Agent Imitation Learning

no code implementations18 Oct 2021 Akshay Dharmavaram, Tejus Gupta, Jiachen Li, Katia P. Sycara

We show that our method (SS-MAIL) outperforms prior state-of-the-art methods on real-world prediction tasks, as well as on custom-designed synthetic experiments.

Imitation Learning

Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting

no code implementations29 Sep 2021 Rui Zhou, HongYu Zhou, Huidong Gao, Masayoshi Tomizuka, Jiachen Li, Zhuo Xu

Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.

Trajectory Forecasting

ConvMLP: Hierarchical Convolutional MLPs for Vision

4 code implementations9 Sep 2021 Jiachen Li, Ali Hassani, Steven Walton, Humphrey Shi

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods.

Ranked #8 on Image Classification on Flowers-102 (using extra training data)

Image Classification Instance Segmentation +3

LOKI: Long Term and Key Intentions for Trajectory Prediction

no code implementations ICCV 2021 Harshayu Girase, Haiming Gang, Srikanth Malla, Jiachen Li, Akira Kanehara, Karttikeya Mangalam, Chiho Choi

We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction.

Autonomous Driving Trajectory Prediction

RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting

no code implementations ICCV 2021 Jiachen Li, Fan Yang, Hengbo Ma, Srikanth Malla, Masayoshi Tomizuka, Chiho Choi

Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.

Motion Forecasting Trajectory Prediction

Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot

no code implementations2 Aug 2021 Cheng Gong, Zirui Li, Xingyu Zhou, Jiachen Li, Jianwei Gong, Junhui Zhou

Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility.


Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

no code implementations24 Jun 2021 Lianzhen Wei, Zirui Li, Jianwei Gong, Cheng Gong, Jiachen Li

Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years.

Autonomous Driving

MSN: Efficient Online Mask Selection Network for Video Instance Segmentation

1 code implementation19 Jun 2021 Vidit Goel, Jiachen Li, Shubhika Garg, Harsh Maheshwari, Humphrey Shi

Our method improves the masks from segmentation and propagation branches in an online manner using the Mask Selection Network (MSN) hence limiting the noise accumulation during mask tracking.

Instance Segmentation Segmentation +4

Spectral Temporal Graph Neural Network for Trajectory Prediction

no code implementations5 Jun 2021 Defu Cao, Jiachen Li, Hengbo Ma, Masayoshi Tomizuka

To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain.

Autonomous Vehicles Motion Forecasting +1

RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection

no code implementations26 May 2021 Jiachen Li, Yuan Lin, Rongrong Liu, Chiu Man Ho, Humphrey Shi

Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors.

Scene Text Detection Segmentation +1

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

1 code implementation29 Apr 2021 Jiachen Li, Bowen Cheng, Rogerio Feris, JinJun Xiong, Thomas S. Huang, Wen-mei Hwu, Humphrey Shi

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric.

Object object-detection +1

Escaping the Big Data Paradigm with Compact Transformers

8 code implementations12 Apr 2021 Ali Hassani, Steven Walton, Nikhil Shah, Abulikemu Abuduweili, Jiachen Li, Humphrey Shi

Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.

 Ranked #1 on Image Classification on Flowers-102 (using extra training data)

Fine-Grained Image Classification Superpixel Image Classification

Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking

no code implementations18 Feb 2021 Jiachen Li, Hengbo Ma, Zhihao Zhang, Jinning Li, Masayoshi Tomizuka

Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent.

Autonomous Vehicles Navigate +2

Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation

no code implementations1 Jan 2021 Jiaxian Guo, Jiachen Li, Mingming Gong, Huan Fu, Kun Zhang, DaCheng Tao

Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.

Translation Unsupervised Image-To-Image Translation

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

no code implementations CVPR 2021 Chiho Choi, Joon Hee Choi, Jiachen Li, Srikanth Malla

At test time, a single input modality (e. g., LiDAR data) is required to generate predictions from the input perspective (i. e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities.

Autonomous Driving Trajectory Prediction

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

no code implementations1 Apr 2020 Chiho Choi, Joon Hee Choi, Srikanth Malla, Jiachen Li

At test time, a single input modality (e. g., LiDAR data) is required to generate predictions from the input perspective (i. e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities.

Autonomous Driving Future prediction +1

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning

no code implementations NeurIPS 2020 Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi

In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents.

Autonomous Driving Decision Making +2

Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

no code implementations14 Feb 2020 Jiachen Li, Hengbo Ma, Zhihao Zhang, Masayoshi Tomizuka

Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios.

Autonomous Vehicles Navigate +2

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

1 code implementation25 Jun 2019 Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, JinJun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen

Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.

object-detection Object Detection

Conditional Generative Neural System for Probabilistic Trajectory Prediction

no code implementations5 May 2019 Jiachen Li, Hengbo Ma, Masayoshi Tomizuka

Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control.

Autonomous Vehicles Decision Making +3

Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling

no code implementations2 May 2019 Jiachen Li, Hengbo Ma, Wei Zhan, Masayoshi Tomizuka

In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task.

Trajectory Prediction

Text Guided Person Image Synthesis

no code implementations CVPR 2019 Xingran Zhou, Siyu Huang, Bin Li, Yingming Li, Jiachen Li, Zhongfei Zhang

This paper presents a novel method to manipulate the visual appearance (pose and attribute) of a person image according to natural language descriptions.

Attribute Image Generation +1

Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning

no code implementations4 Apr 2019 Jiachen Li, Hengbo Ma, Masayoshi Tomizuka

In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite.

Autonomous Vehicles Decision Making +2

Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios

no code implementations10 Sep 2018 Wei Zhan, Liting Sun, Yeping Hu, Jiachen Li, Masayoshi Tomizuka

Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.

Autonomous Vehicles Decision Making

Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real

no code implementations9 Sep 2018 Jiachen Li, Hengbo Ma, Wei Zhan, Masayoshi Tomizuka

Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making.

Autonomous Driving Decision Making +1

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