Search Results for author: Longteng Guo

Found 15 papers, 6 papers with code

Unveiling Parts Beyond Objects:Towards Finer-Granularity Referring Expression Segmentation

1 code implementation13 Dec 2023 Wenxuan Wang, Tongtian Yue, Yisi Zhang, Longteng Guo, Xingjian He, Xinlong Wang, Jing Liu

To foster future research into fine-grained visual grounding, our benchmark RefCOCOm, the MRES-32M dataset and model UniRES will be publicly available at https://github. com/Rubics-Xuan/MRES

Descriptive Object +3

EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE

no code implementations23 Aug 2023 Junyi Chen, Longteng Guo, Jia Sun, Shuai Shao, Zehuan Yuan, Liang Lin, Dongyu Zhang

Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed.

Image-text matching Question Answering +5

Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner

1 code implementation19 May 2023 Zikang Liu, Sihan Chen, Longteng Guo, Handong Li, Xingjian He, Jing Liu

In this paper, we propose a novel method called Joint QA and DC GEneration (JADE), which utilizes a pre-trained multimodal model and easily-crawled image-text pairs to automatically generate and filter large-scale VQA and dense captioning datasets.

Dense Captioning Image Captioning +4

VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset

1 code implementation17 Apr 2023 Sihan Chen, Xingjian He, Longteng Guo, Xinxin Zhu, Weining Wang, Jinhui Tang, Jing Liu

Different from widely-studied vision-language pretraining models, VALOR jointly models relationships of vision, audio and language in an end-to-end manner.

 Ranked #1 on Video Captioning on VATEX (using extra training data)

Audio captioning Audio-Video Question Answering (AVQA) +16

MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning

no code implementations9 Oct 2022 Zijia Zhao, Longteng Guo, Xingjian He, Shuai Shao, Zehuan Yuan, Jing Liu

Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover.

Question Answering Representation Learning +5

OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation

2 code implementations1 Jul 2021 Jing Liu, Xinxin Zhu, Fei Liu, Longteng Guo, Zijia Zhao, Mingzhen Sun, Weining Wang, Hanqing Lu, Shiyu Zhou, Jiajun Zhang, Jinqiao Wang

In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.

Audio to Text Retrieval Cross-Modal Retrieval +3

CPTR: Full Transformer Network for Image Captioning

no code implementations26 Jan 2021 Wei Liu, Sihan Chen, Longteng Guo, Xinxin Zhu, Jing Liu

Besides, we provide detailed visualizations of the self-attention between patches in the encoder and the "words-to-patches" attention in the decoder thanks to the full Transformer architecture.

Image Captioning

Fast Sequence Generation with Multi-Agent Reinforcement Learning

no code implementations24 Jan 2021 Longteng Guo, Jing Liu, Xinxin Zhu, Hanqing Lu

These models are autoregressive in that they generate each word by conditioning on previously generated words, which leads to heavy latency during inference.

Image Captioning Machine Translation +5

AutoCaption: Image Captioning with Neural Architecture Search

no code implementations16 Dec 2020 Xinxin Zhu, Weining Wang, Longteng Guo, Jing Liu

The whole process involves a visual understanding module and a language generation module, which brings more challenges to the design of deep neural networks than other tasks.

Image Captioning Neural Architecture Search +1

Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning

no code implementations10 May 2020 Longteng Guo, Jing Liu, Xinxin Zhu, Xingjian He, Jie Jiang, Hanqing Lu

In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL).

Image Captioning Machine Translation +3

Vatex Video Captioning Challenge 2020: Multi-View Features and Hybrid Reward Strategies for Video Captioning

no code implementations17 Oct 2019 Xinxin Zhu, Longteng Guo, Peng Yao, Shichen Lu, Wei Liu, Jing Liu

This report describes our solution for the VATEX Captioning Challenge 2020, which requires generating descriptions for the videos in both English and Chinese languages.

Video Captioning

Aligning Linguistic Words and Visual Semantic Units for Image Captioning

1 code implementation6 Aug 2019 Longteng Guo, Jing Liu, Jinhui Tang, Jiangwei Li, Wei Luo, Hanqing Lu

Image captioning attempts to generate a sentence composed of several linguistic words, which are used to describe objects, attributes, and interactions in an image, denoted as visual semantic units in this paper.

Attribute Image Captioning +2

MSCap: Multi-Style Image Captioning With Unpaired Stylized Text

no code implementations CVPR 2019 Longteng Guo, Jing Liu, Peng Yao, Jiangwei Li, Hanqing Lu

The discriminator and the generator are trained in an adversarial manner to enable more natural and human-like captions.

Image Captioning Sentence

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