Search Results for author: Jiuxiang Gu

Found 56 papers, 17 papers with code

DocTime: A Document-level Temporal Dependency Graph Parser

no code implementations NAACL 2022 Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, Rajiv Jain

We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph.

Differential Privacy Mechanisms in Neural Tangent Kernel Regression

no code implementations18 Jul 2024 Jiuxiang Gu, YIngyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song

Training data privacy is a fundamental problem in modern Artificial Intelligence (AI) applications, such as face recognition, recommendation systems, language generation, and many others, as it may contain sensitive user information related to legal issues.

Toward Infinite-Long Prefix in Transformer

1 code implementation20 Jun 2024 Jiuxiang Gu, YIngyu Liang, Zhenmei Shi, Zhao Song, Chiwun Yang

Prompting and contextual-based fine-tuning methods, which we call Prefix Learning, have been proposed to enhance the performance of language models on various downstream tasks that can match full parameter fine-tuning.

ARTIST: Improving the Generation of Text-rich Images by Disentanglement

no code implementations17 Jun 2024 Jianyi Zhang, Yufan Zhou, Jiuxiang Gu, Curtis Wigington, Tong Yu, Yiran Chen, Tong Sun, Ruiyi Zhang

Diffusion models have demonstrated exceptional capabilities in generating a broad spectrum of visual content, yet their proficiency in rendering text is still limited: they often generate inaccurate characters or words that fail to blend well with the underlying image.

Disentanglement Image Generation

DocSynthv2: A Practical Autoregressive Modeling for Document Generation

no code implementations12 Jun 2024 Sanket Biswas, Rajiv Jain, Vlad I. Morariu, Jiuxiang Gu, Puneet Mathur, Curtis Wigington, Tong Sun, Josep Lladós

While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge.

TRINS: Towards Multimodal Language Models that Can Read

no code implementations CVPR 2024 Ruiyi Zhang, Yanzhe Zhang, Jian Chen, Yufan Zhou, Jiuxiang Gu, Changyou Chen, Tong Sun

In this work, we introduce TRINS: a Text-Rich image INStruction dataset, with the objective of enhancing the reading ability of the multimodal large language model.

Language Modelling Large Language Model +1

Tensor Attention Training: Provably Efficient Learning of Higher-order Transformers

no code implementations26 May 2024 Jiuxiang Gu, YIngyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou

Tensor Attention, a multi-view attention that is able to capture high-order correlations among multiple modalities, can overcome the representational limitations of classical matrix attention.

Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective

no code implementations26 May 2024 Jiuxiang Gu, YIngyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou

We prove that if the target distribution is a $k$-mixture of Gaussians, the density of the entire diffusion process will also be a $k$-mixture of Gaussians.

Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond

no code implementations6 May 2024 Jiuxiang Gu, Chenyang Li, YIngyu Liang, Zhenmei Shi, Zhao Song

The softmax activation function plays a crucial role in the success of large language models (LLMs), particularly in the self-attention mechanism of the widely adopted Transformer architecture.

SOHES: Self-supervised Open-world Hierarchical Entity Segmentation

no code implementations18 Apr 2024 Shengcao Cao, Jiuxiang Gu, Jason Kuen, Hao Tan, Ruiyi Zhang, Handong Zhao, Ani Nenkova, Liang-Yan Gui, Tong Sun, Yu-Xiong Wang

Using raw images as the sole training data, our method achieves unprecedented performance in self-supervised open-world segmentation, marking a significant milestone towards high-quality open-world entity segmentation in the absence of human-annotated masks.


Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning

2 code implementations15 Feb 2024 Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, Tianyi Zhou

This paper introduces Selective Reflection-Tuning, a novel paradigm that synergizes a teacher LLM's reflection and introspection for improving existing data quality with the data selection capability of the student LLM, to automatically refine existing instruction-tuning data.

Data Augmentation Instruction Following

Fourier Circuits in Neural Networks: Unlocking the Potential of Large Language Models in Mathematical Reasoning and Modular Arithmetic

no code implementations12 Feb 2024 Jiuxiang Gu, Chenyang Li, YIngyu Liang, Zhenmei Shi, Zhao Song, Tianyi Zhou

Our research presents a thorough analytical characterization of the features learned by stylized one-hidden layer neural networks and one-layer Transformers in addressing this task.

2k Mathematical Reasoning

LRM: Large Reconstruction Model for Single Image to 3D

1 code implementation8 Nov 2023 Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, Hao Tan

We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds.

Image to 3D

Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances

no code implementations25 Oct 2023 Zhendong Chu, Ruiyi Zhang, Tong Yu, Rajiv Jain, Vlad I Morariu, Jiuxiang Gu, Ani Nenkova

To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate.


Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning

2 code implementations18 Oct 2023 Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Heng Huang, Jiuxiang Gu, Tianyi Zhou

Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation.

Natural Language Understanding

LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

1 code implementation29 Jun 2023 Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, Yufan Zhou, Nedim Lipka, Diyi Yang, Tong Sun

Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans.

16k Image Captioning +3

AIMS: All-Inclusive Multi-Level Segmentation

1 code implementation28 May 2023 Lu Qi, Jason Kuen, Weidong Guo, Jiuxiang Gu, Zhe Lin, Bo Du, Yu Xu, Ming-Hsuan Yang

Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-interest selections remains unsolved.

Image Segmentation Segmentation +1

LayerDoc: Layer-wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents

no code implementations IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 Puneet Mathur, Rajiv Jain, Ashutosh Mehra, Jiuxiang Gu, Franck Dernoncourt, Anandhavelu N, Quan Tran, Verena Kaynig-Fittkau, Ani Nenkova, Dinesh Manocha, Vlad I. Morariu

Experiments show that our approach outperforms competitive baselines by 10-15% on three diverse datasets of forms and mobile app screen layouts for the tasks of spatial region classification, higher-order group identification, layout hierarchy extraction, reading order detection, and word grouping.

Reading Order Detection

High Quality Entity Segmentation

no code implementations ICCV 2023 Lu Qi, Jason Kuen, Tiancheng Shen, Jiuxiang Gu, Wenbo Li, Weidong Guo, Jiaya Jia, Zhe Lin, Ming-Hsuan Yang

Given the high-quality and -resolution nature of the dataset, we propose CropFormer which is designed to tackle the intractability of instance-level segmentation on high-resolution images.

Image Segmentation Segmentation +1

MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding

no code implementations27 Nov 2022 Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad I. Morariu

In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.

High-Quality Entity Segmentation

1 code implementation10 Nov 2022 Lu Qi, Jason Kuen, Weidong Guo, Tiancheng Shen, Jiuxiang Gu, Jiaya Jia, Zhe Lin, Ming-Hsuan Yang

It improves mask prediction by fusing high-res image crops that provide more fine-grained image details and the full image.

Image Segmentation Segmentation +2

User-Entity Differential Privacy in Learning Natural Language Models

1 code implementation1 Nov 2022 Phung Lai, NhatHai Phan, Tong Sun, Rajiv Jain, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios

In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs).

Improving the Reliability for Confidence Estimation

no code implementations13 Oct 2022 Haoxuan Qu, Yanchao Li, Lin Geng Foo, Jason Kuen, Jiuxiang Gu, Jun Liu

Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models.

Image Classification Meta-Learning +1

Meta Spatio-Temporal Debiasing for Video Scene Graph Generation

no code implementations23 Jul 2022 Li Xu, Haoxuan Qu, Jason Kuen, Jiuxiang Gu, Jun Liu

Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video.

Graph Generation Meta-Learning +2

Towards Language-Free Training for Text-to-Image Generation

no code implementations CVPR 2022 Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun

One of the major challenges in training text-to-image generation models is the need of a large number of high-quality text-image pairs.

Zero-Shot Text-to-Image Generation

CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation

1 code implementation9 Dec 2021 Lu Qi, Jason Kuen, Zhe Lin, Jiuxiang Gu, Fengyun Rao, Dian Li, Weidong Guo, Zhen Wen, Ming-Hsuan Yang, Jiaya Jia

To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data.

object-detection Object Detection +2

Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

1 code implementation CVPR 2022 Dat Huynh, Jason Kuen, Zhe Lin, Jiuxiang Gu, Ehsan Elhamifar

To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images.

Instance Segmentation Semantic Segmentation

Bit-aware Randomized Response for Local Differential Privacy in Federated Learning

no code implementations29 Sep 2021 Phung Lai, Hai Phan, Li Xiong, Khang Phuc Tran, My Thai, Tong Sun, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Rajiv Jain

In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).

Federated Learning Image Classification

Open-World Entity Segmentation

2 code implementations29 Jul 2021 Lu Qi, Jason Kuen, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia

By removing the need of class label prediction, the models trained for such task can focus more on improving segmentation quality.

Image Manipulation Image Segmentation +2

Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection

no code implementations CVPR 2021 Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu

The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network.

Action Unit Detection Facial Action Unit Detection +1

SelfDoc: Self-Supervised Document Representation Learning

no code implementations CVPR 2021 Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad I. Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, Hongfu Liu

For downstream usage, we propose a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals.

Representation Learning

Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models

no code implementations NAACL 2021 Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu

These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample.

Self-Supervised Relationship Probing

no code implementations NeurIPS 2020 Jiuxiang Gu, Jason Kuen, Shafiq Joty, Jianfei Cai, Vlad Morariu, Handong Zhao, Tong Sun

Structured representations of images that model visual relationships are beneficial for many vision and vision-language applications.

Contrastive Learning Language Modelling +1

UNISON: Unpaired Cross-lingual Image Captioning

no code implementations3 Oct 2020 Jiahui Gao, Yi Zhou, Philip L. H. Yu, Shafiq Joty, Jiuxiang Gu

In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language.

Caption Generation Image Captioning +3

Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning

no code implementations ECCV 2020 Xiangxi Shi, Xu Yang, Jiuxiang Gu, Shafiq Joty, Jianfei Cai

In this paper, we propose a novel visual encoder to explicitly distinguish viewpoint changes from semantic changes in the change captioning task.

Reinforcement Learning (RL)

Watch It Twice: Video Captioning with a Refocused Video Encoder

no code implementations21 Jul 2019 Xiangxi Shi, Jianfei Cai, Shafiq Joty, Jiuxiang Gu

With the rapid growth of video data and the increasing demands of various applications such as intelligent video search and assistance toward visually-impaired people, video captioning task has received a lot of attention recently in computer vision and natural language processing fields.

Video Captioning

Scene Graph Generation with External Knowledge and Image Reconstruction

no code implementations CVPR 2019 Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, Mingyang Ling

Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\etc.

Graph Generation Image Reconstruction +6

Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction

no code implementations8 Jul 2018 Xiangxi Shi, Jianfei Cai, Jiuxiang Gu, Shafiq Joty

In this paper, we propose a boundary-aware hierarchical language decoder for video captioning, which consists of a high-level GRU based language decoder, working as a global (caption-level) language model, and a low-level GRU based language decoder, working as a local (phrase-level) language model.

Decoder Language Modelling +4

Unpaired Image Captioning by Language Pivoting

no code implementations ECCV 2018 Jiuxiang Gu, Shafiq Joty, Jianfei Cai, Gang Wang

Image captioning is a multimodal task involving computer vision and natural language processing, where the goal is to learn a mapping from the image to its natural language description.

Image Captioning Sentence

Stack-Captioning: Coarse-to-Fine Learning for Image Captioning

1 code implementation11 Sep 2017 Jiuxiang Gu, Jianfei Cai, Gang Wang, Tsuhan Chen

On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem.

Decoder Image Captioning +1

Recent Advances in Convolutional Neural Networks

no code implementations22 Dec 2015 Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing.

speech-recognition Speech Recognition

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