1 code implementation • NAACL 2022 • Benfeng Xu, Quan Wang, Yajuan Lyu, Yabing Shi, Yong Zhu, Jie Gao, Zhendong Mao
Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations. While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples. We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.
Document-level Relation Extraction Joint Entity and Relation Extraction +2
1 code implementation • 19 Apr 2024 • Fengyi Fu, Shancheng Fang, Weidong Chen, Zhendong Mao
Furthermore, a batch attention module is also proposed in this paper to alleviate the problem of missing sentimental samples, caused by the data imbalance, which is common in live videos as the popularity of videos varies.
1 code implementation • 5 Apr 2024 • Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song, Ying WEI, Defu Lian, Zhendong Mao
Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is a crucial property for multi-aspect controllable text generation (MCTG) methods.
no code implementations • 1 Mar 2024 • Mengqi Huang, Zhendong Mao, Mingcong Liu, Qian He, Yongdong Zhang
However, the inherent entangled influence scope of pseudo-words with the given text results in a dual-optimum paradox, i. e., the similarity of the given subjects and the controllability of the given text could not be optimal simultaneously.
no code implementations • 22 Feb 2024 • Hao Li, Mengqi Huang, Lei Zhang, Bo Hu, Yi Liu, Zhendong Mao
GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes.
1 code implementation • 1 Jan 2024 • Yihan Chen, Benfeng Xu, Quan Wang, Yi Liu, Zhendong Mao
While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constraints that might be entailed in various instructions.
no code implementations • 25 Nov 2023 • Fengyi Fu, Lei Zhang, Quan Wang, Zhendong Mao
Then we propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy, respectively improving the emotion perception and response generation.
no code implementations • 23 Nov 2023 • Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, Yongdong Zhang
In this paper, the inherent discrepancies are manifested in two aspects, namely, accuracy of data annotation and diversity of potential annotations.
no code implementations • 22 Nov 2023 • Chiwei Zhu, Benfeng Xu, Quan Wang, Yongdong Zhang, Zhendong Mao
As large language models attract increasing attention and find widespread application, concurrent challenges of reliability also arise at the same time.
no code implementations • 14 Nov 2023 • Ting Wang, Weidong Chen, Yuanhe Tian, Yan Song, Zhendong Mao
Having the difficulty of solving the semantic gap between images and texts for the image captioning task, conventional studies in this area paid some attention to treating semantic concepts as a bridge between the two modalities and improved captioning performance accordingly.
1 code implementation • 24 Oct 2023 • Jiaang Li, Quan Wang, Yi Liu, Licheng Zhang, Zhendong Mao
We analyze this phenomenon and reveal that entity codes, the quantization outcomes for expressing entities, have higher entropy at the code level and Jaccard distance at the codeword level under random entity quantization.
1 code implementation • 23 Oct 2023 • Tianqi Zhong, Quan Wang, Jingxuan Han, Yongdong Zhang, Zhendong Mao
Then we design a novel attribute distribution reconstruction method to balance the obtained distributions and use the reconstructed distributions to guide language models for generation, effectively avoiding the issue of Attribute Collapse.
no code implementations • 1 Jul 2023 • Zhuowei Chen, Shancheng Fang, Wei Liu, Qian He, Mengqi Huang, Yongdong Zhang, Zhendong Mao
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity for conditioned face images.
2 code implementations • 24 May 2023 • Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Yongdong Zhang, Zhendong Mao
The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts.
1 code implementation • CVPR 2023 • Mengqi Huang, Zhendong Mao, Quan Wang, Yongdong Zhang
Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook.
1 code implementation • CVPR 2023 • Mengqi Huang, Zhendong Mao, Zhuowei Chen, Yongdong Zhang
Existing vector quantization (VQ) based autoregressive models follow a two-stage generation paradigm that first learns a codebook to encode images as discrete codes, and then completes generation based on the learned codebook.
1 code implementation • 1 Apr 2023 • Jiaang Li, Quan Wang, Zhendong Mao
Relation prediction on knowledge graphs (KGs) is a key research topic.
1 code implementation • 24 Mar 2023 • Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She, Yongdong Zhang
In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs.
1 code implementation • CVPR 2023 • Zheren Fu, Zhendong Mao, Yan Song, Yongdong Zhang
Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities.
no code implementations • CVPR 2023 • Yuchen Ren, Zhendong Mao, Shancheng Fang, Yan Lu, Tong He, Hao Du, Yongdong Zhang, Wanli Ouyang
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
1 code implementation • 29 Nov 2022 • Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, Yongdong Zhang
They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations.
1 code implementation • 19 Nov 2022 • Shancheng Fang, Zhendong Mao, Hongtao Xie, Yuxin Wang, Chenggang Yan, Yongdong Zhang
In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input.
Ranked #4 on Text Spotting on SCUT-CTW1500
1 code implementation • 16 Nov 2022 • Wei Tang, Benfeng Xu, Yuyue Zhao, Zhendong Mao, Yifeng Liu, Yong Liao, Haiyong Xie
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.
Ranked #1 on Relation Extraction on WebNLG
1 code implementation • 20 Oct 2022 • Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang
In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task.
no code implementations • 3 Sep 2022 • Mengqi Huang, Zhendong Mao, Penghui Wang, Quan Wang, Yongdong Zhang
Text-to-image generation aims at generating realistic images which are semantically consistent with the given text.
1 code implementation • CVPR 2022 • Kun Zhang, Zhendong Mao, Quan Wang, Yongdong Zhang
Image-text matching, as a fundamental task, bridges the gap between vision and language.
no code implementations • CVPR 2021 • Rui Sun, Yihao Li, Tianzhu Zhang, Zhendong Mao, Feng Wu, Yongdong Zhang
First, to the best of our knowledge, this is the first work to formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder.
3 code implementations • CVPR 2021 • Shancheng Fang, Hongtao Xie, Yuxin Wang, Zhendong Mao, Yongdong Zhang
Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively.
3 code implementations • 20 Feb 2021 • Benfeng Xu, Quan Wang, Yajuan Lyu, Yong Zhu, Zhendong Mao
Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
Ranked #3 on Relation Extraction on DocRED
1 code implementation • 17 Dec 2020 • Xi Zhu, Zhendong Mao, Chunxiao Liu, Peng Zhang, Bin Wang, Yongdong Zhang
Our method can compensate for the data biases by generating balanced data without introducing external annotations.
no code implementations • 10 Dec 2020 • Zeliang Song, Xiaofei Zhou, Zhendong Mao, Jianlong Tan
Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image.
no code implementations • ACL 2020 • Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, Yongdong Zhang
With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks.
1 code implementation • CVPR 2020 • Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, Yongdong Zhang
The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase.
Ranked #16 on Cross-Modal Retrieval on Flickr30k