1 code implementation • ACL 2022 • Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, Xiaojie Wang
Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence.
no code implementations • COLING 2022 • Ziqin Rao, Fangxiang Feng, Ruifan Li, Xiaojie Wang
Our BGM comprises of an instance embedding module and a bag representation module.
no code implementations • 9 Apr 2024 • Pengfei Zhou, Fangxiang Feng, Xiaojie Wang
To deal with these issues, in this paper, we first adapt a pre-trained latent diffusion model to the image harmonization task to generate the harmonious but potentially blurry initial images.
1 code implementation • 19 Aug 2023 • Fulong Ye, Yuxing Long, Fangxiang Feng, Xiaojie Wang
Referring Expression Generation (REG) aims to generate unambiguous Referring Expressions (REs) for objects in a visual scene, with a dual task of Referring Expression Comprehension (REC) to locate the referred object.
1 code implementation • 23 May 2022 • Bo Yang, Fangxiang Feng, Xiaojie Wang
We also introduce a new metric Cross-Model Distance (CMD) for simultaneously evaluating image quality and image-text consistency.
no code implementations • 3 Apr 2022 • Yuxi Qian, Yuncong Hu, Ruonan Wang, Fangxiang Feng, Xiaojie Wang
It first models semantic, spatial, and implicit visual relations in images by three graph attention networks, then question information is utilized to guide the aggregation process of the three graphs, further, our QD-GFN adopts an object filtering mechanism to remove question-irrelevant objects contained in the image.
no code implementations • Findings (ACL) 2022 • Ruonan Wang, Yuxi Qian, Fangxiang Feng, Xiaojie Wang, Huixing Jiang
Most existing approaches to Visual Question Answering (VQA) answer questions directly, however, people usually decompose a complex question into a sequence of simple sub questions and finally obtain the answer to the original question after answering the sub question sequence(SQS).
1 code implementation • 16 Mar 2022 • Duo Zheng, Fandong Meng, Qingyi Si, Hairun Fan, Zipeng Xu, Jie zhou, Fangxiang Feng, Xiaojie Wang
Visual dialog has witnessed great progress after introducing various vision-oriented goals into the conversation, especially such as GuessWhich and GuessWhat, where the only image is visible by either and both of the questioner and the answerer, respectively.
1 code implementation • ACL 2021 • Ruifan Li, Hao Chen, Fangxiang Feng, Zhanyu Ma, Xiaojie Wang, Eduard Hovy
To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
1 code implementation • ACL 2021 • Tongtong Liu, Fangxiang Feng, Xiaojie Wang
Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task.
1 code implementation • 1 Oct 2020 • Zipeng Xu, Fangxiang Feng, Xiaojie Wang, Yushu Yang, Huixing Jiang, Zhongyuan Wang
In this paper, we propose an Answer-Driven Visual State Estimator (ADVSE) to impose the effects of different answers on visual states.
no code implementations • Neurocomputing 2020 • Ruifan Li, Haoyun Liang, Yihui Shi, Fangxiang Feng, Xiaojie Wang
Abstract The task of paragraph image captioning aims to generate a coherent paragraph describing a given image.
no code implementations • 4 Jul 2013 • Fangxiang Feng, Ruifan Li, Xiaojie Wang
This paper describes our solution to the multi-modal learning challenge of ICML.
11 code implementations • 1 Jul 2013 • Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #12 on Facial Expression Recognition (FER) on FER2013