Search Results for author: Zhengjue Wang

Found 12 papers, 7 papers with code

BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging

1 code implementation ECCV 2020 Ziheng Cheng, Ruiying Lu, Zhengjue Wang, Hao Zhang, Bo Chen, Ziyi Meng, Xin Yuan

This measurement and the modulation masks are fed into our Recurrent Neural Network (RNN) to reconstruct the desired high-speed frames.

Friendly Topic Assistant for Transformer Based Abstractive Summarization

no code implementations EMNLP 2020 Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou

Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.

Abstractive Text Summarization Document Summarization +2

MeaCap: Memory-Augmented Zero-shot Image Captioning

1 code implementation6 Mar 2024 Zequn Zeng, Yan Xie, Hao Zhang, Chiyu Chen, Zhengjue Wang, Bo Chen

The framework of MeaCap achieves the state-of-the-art performance on a series of zero-shot IC settings.

Image Captioning Language Modelling +3

SnapCap: Efficient Snapshot Compressive Video Captioning

no code implementations10 Jan 2024 JianQiao Sun, Yudi Su, Hao Zhang, Ziheng Cheng, Zequn Zeng, Zhengjue Wang, Bo Chen, Xin Yuan

To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap.

Compressive Sensing Video Captioning

ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

1 code implementation CVPR 2023 Zequn Zeng, Hao Zhang, Zhengjue Wang, Ruiying Lu, Dongsheng Wang, Bo Chen

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data.

Image Captioning Language Modelling

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering

1 code implementation ACL 2021 Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang, Bo Chen, Mingyuan Zhou

As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.

Clustering Language Modelling

Memory-Efficient Network for Large-scale Video Compressive Sensing

2 code implementations CVPR 2021 Ziheng Cheng, Bo Chen, Guanliang Liu, Hao Zhang, Ruiying Lu, Zhengjue Wang, Xin Yuan

With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.

Compressive Sensing Demosaicking +1

MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing

2 code implementations CVPR 2021 Zhengjue Wang, Hao Zhang, Ziheng Cheng, Bo Chen, Xin Yuan

To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.

Compressive Sensing Video Compressive Sensing

Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network

no code implementations NeurIPS 2020 Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou

To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.

Topic Models

Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling

1 code implementation ICLR 2020 Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.

Generative Adversarial Network

VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning

no code implementations ICLR 2019 Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network.

Image Generation Retrieval +3

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