Search Results for author: Lu Jiang

Found 37 papers, 19 papers with code

SimAug: Learning Robust Representations from Simulation for Trajectory Prediction

no code implementations ECCV 2020 Junwei Liang, Lu Jiang, Alexander Hauptmann

We approach this problem through the real-data-free setting in which the model is trained only on 3D simulation data and applied out-of-the-box to a wide variety of real cameras.

Trajectory Prediction

ViTGAN: Training GANs with Vision Transformers

1 code implementation9 Jul 2021 Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu

Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases.

Image Generation

Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

no code implementations8 Jun 2021 Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey

Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT).

Low-Resource Neural Machine Translation Translation

Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent

no code implementations19 Sep 2020 Xiaosa Zhao, Kunpeng Liu, Wei Fan, Lu Jiang, Xiaowei Zhao, Minghao Yin, Yanjie Fu

To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy.

Feature Selection

Text as Neural Operator: Image Manipulation by Text Instruction

no code implementations11 Aug 2020 Tianhao Zhang, Hung-Yu Tseng, Lu Jiang, Honglak Lee, Irfan Essa, Weilong Yang

In this image generation task, the inputs are a reference image and an instruction in natural language that describes desired modifications to the input image.

Image Captioning Image Generation +1

RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval

no code implementations ECCV 2020 Hung-Yu Tseng, Hsin-Ying Lee, Lu Jiang, Ming-Hsuan Yang, Weilong Yang

Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing.

Image Generation

Controllable and Progressive Image Extrapolation

no code implementations25 Dec 2019 Yijun Li, Lu Jiang, Ming-Hsuan Yang

Image extrapolation aims at expanding the narrow field of view of a given image patch.

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

1 code implementation CVPR 2020 Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann

The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.

Autonomous Driving Human motion prediction +5

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

1 code implementation ICML 2020 Lu Jiang, Di Huang, Mason Liu, Weilong Yang

Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting.

Image Classification

Confident Learning: Estimating Uncertainty in Dataset Labels

2 code implementations31 Oct 2019 Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang

Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence.

Learning with noisy labels Sentiment Analysis

Feature Partitioning for Efficient Multi-Task Architectures

no code implementations ICLR 2020 Alejandro Newell, Lu Jiang, Chong Wang, Li-Jia Li, Jia Deng

Multi-task learning holds the promise of less data, parameters, and time than training of separate models.

Multi-Task Learning

Robust Neural Machine Translation with Doubly Adversarial Inputs

1 code implementation ACL 2019 Yong Cheng, Lu Jiang, Wolfgang Macherey

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input.

Machine Translation Translation

State-aware Re-identification Feature for Multi-target Multi-camera Tracking

no code implementations4 Jun 2019 Peng Li, Jiabin Zhang, Zheng Zhu, Yanwei Li, Lu Jiang, Guan Huang

Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras.

Eidetic 3D LSTM: A Model for Video Prediction and Beyond

2 code implementations ICLR 2019 Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei

We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.

Activity Recognition Video Prediction

Revisiting EmbodiedQA: A Simple Baseline and Beyond

no code implementations8 Apr 2019 Yu Wu, Lu Jiang, Yi Yang

In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions.

Embodied Question Answering Question Answering

Let's Transfer Transformations of Shared Semantic Representations

2 code implementations2 Mar 2019 Nam Vo, Lu Jiang, James Hays

In this work we show how one can learn transformations with no training examples by learning them on another domain and then transfer to the target domain.

Image Retrieval Zero-Shot Learning

Contrastive Adaptation Network for Unsupervised Domain Adaptation

1 code implementation CVPR 2019 Guoliang Kang, Lu Jiang, Yi Yang, Alexander G. Hauptmann

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain.

Unsupervised Domain Adaptation

Composing Text and Image for Image Retrieval - An Empirical Odyssey

3 code implementations CVPR 2019 Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays

In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image.

Image Retrieval Image Retrieval with Multi-Modal Query

Focal Visual-Text Attention for Visual Question Answering

2 code implementations CVPR 2018 Junwei Liang, Lu Jiang, Liangliang Cao, Li-Jia Li, Alexander Hauptmann

Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering.

Memex Question Answering Question Answering +1

Decoupled Novel Object Captioner

1 code implementation11 Apr 2018 Yu Wu, Linchao Zhu, Lu Jiang, Yi Yang

Thus, the sequence model can be decoupled from the novel object descriptions.

Image Captioning

Graph Distillation for Action Detection with Privileged Modalities

1 code implementation ECCV 2018 Zelun Luo, Jun-Ting Hsieh, Lu Jiang, Juan Carlos Niebles, Li Fei-Fei

We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available.

Action Classification Action Detection +1

MemexQA: Visual Memex Question Answering

no code implementations4 Aug 2017 Lu Jiang, Junwei Liang, Liangliang Cao, Yannis Kalantidis, Sachin Farfade, Alexander Hauptmann

This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection.

Memex Question Answering Question Answering +1

Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning

1 code implementation16 Jul 2016 Junwei Liang, Lu Jiang, Deyu Meng, Alexander Hauptmann

Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community.

Strategies for Searching Video Content with Text Queries or Video Examples

no code implementations17 Jun 2016 Shoou-I Yu, Yi Yang, Zhongwen Xu, Shicheng Xu, Deyu Meng, Zexi Mao, Zhigang Ma, Ming Lin, Xuanchong Li, Huan Li, Zhenzhong Lan, Lu Jiang, Alexander G. Hauptmann, Chuang Gan, Xingzhong Du, Xiaojun Chang

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.

Event Detection Video Retrieval

What Objective Does Self-paced Learning Indeed Optimize?

no code implementations19 Nov 2015 Deyu Meng, Qian Zhao, Lu Jiang

Self-paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals.

Self-Paced Learning with Diversity

no code implementations NeurIPS 2014 Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann

Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training.

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