Search Results for author: Yuejian Fang

Found 12 papers, 7 papers with code

Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation

no code implementations ECNLP (ACL) 2022 Zeming Wang, Yanyan Zou, Yuejian Fang, Hongshen Chen, Mian Ma, Zhuoye Ding, Bo Long

As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.


Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models

1 code implementation20 Feb 2024 Che Zhang, Zhenyang Xiao, Chengcheng Han, Yixin Lian, Yuejian Fang

After integrating the original CoT data and checking-correction data for training, we observe that models could improve their self-checking capabilities, thereby enhancing their self-correction capacity and eliminating the need for external feedback or ground truth labels to ascertain the endpoint of correction.

Mathematical Reasoning

Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning

1 code implementation7 May 2023 Shengfang Zhai, Yinpeng Dong, Qingni Shen, Shi Pu, Yuejian Fang, Hang Su

To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels.

Backdoor Attack backdoor defense +2

A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning

1 code implementation COLING 2022 Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen

Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages.

text-classification Text Classification +3

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching

1 code implementation Findings (NAACL) 2022 Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang

Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.

Contrastive Learning Knowledge Distillation +3

NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

1 code implementation20 Jul 2022 Chenfei Wu, Jian Liang, Xiaowei Hu, Zhe Gan, JianFeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan

In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos.

Image Outpainting Text-to-Image Generation +1

NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

1 code implementation24 Nov 2021 Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan

To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively.

Text-to-Image Generation Text-to-Video Generation +2

Hybrid Reasoning Network for Video-based Commonsense Captioning

1 code implementation5 Aug 2021 Weijiang Yu, Jian Liang, Lei Ji, Lu Li, Yuejian Fang, Nong Xiao, Nan Duan

Firstly, we develop multi-commonsense learning for semantic-level reasoning by jointly training different commonsense types in a unified network, which encourages the interaction between the clues of multiple commonsense descriptions, event-wise captions and videos.


Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training

no code implementations16 Aug 2019 Gen Li, Nan Duan, Yuejian Fang, Ming Gong, Daxin Jiang, Ming Zhou

We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner.

Ranked #5 on Image-to-Text Retrieval on MS COCO (Recall@10 metric)

Image-text matching Image-to-Text Retrieval +5

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