no code implementations • 29 May 2024 • Zhe Hu, Tuo Liang, Jing Li, Yiren Lu, Yunlai Zhou, Yiran Qiao, Jing Ma, Yu Yin
Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics.
no code implementations • 31 Oct 2023 • Zhe Hu, Hou Pong Chan, Yu Yin
Argument generation is a challenging task in natural language processing, which requires rigorous reasoning and proper content organization.
no code implementations • 13 Aug 2023 • Haichao Zhang, Can Qin, Yu Yin, Yun Fu
This approach can serve as a plug-and-play data generation and augmentation module for existing camouflaged object detection tasks and provides a novel way to introduce more diversity and distributions into current camouflage datasets.
no code implementations • CVPR 2023 • Yu Yin, Kamran Ghasedi, HsiangTao Wu, Jiaolong Yang, Xin Tong, Yun Fu
Furthermore, our method leverages explicit and implicit 3D regularizations using the in-domain neighborhood samples around the optimized latent code to remove geometrical and visual artifacts.
1 code implementation • 12 Dec 2021 • Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu
Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains.
no code implementations • EMNLP 2021 • Zhe Hu, Zuohui Fu, Yu Yin, Gerard de Melo
Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations.
no code implementations • 15 Jan 2021 • Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, Shijin Wang
In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.
no code implementations • 12 Dec 2020 • Yu Yin, Joseph P. Robinson, Yun Fu
Typically, humans are covered by a blanket when resting, for which we propose a multimodal approach to uncover the subjects so their bodies at rest can be viewed without the occlusion of the blankets above.
no code implementations • 7 Dec 2020 • Yu Yin, Joseph P. Robinson, Songyao Jiang, Yue Bai, Can Qin, Yun Fu
Even as impressive milestones are achieved in synthesizing faces, the importance of preserving identity is needed in practice and should not be overlooked.
no code implementations • 14 Sep 2020 • Yue Bai, Zhiqiang Tao, Lichen Wang, Sheng Li, Yu Yin, Yun Fu
Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.
no code implementations • 28 Jul 2020 • Joseph P. Robinson, Zaid Khan, Yu Yin, Ming Shao, Yun Fu
Thus, to narrow the gap between research and reality and enhance the power of kinship recognition systems, we extend FIW with multimedia (MM) data (i. e., video, audio, and text captions).
1 code implementation • 17 Feb 2020 • Yu Yin, Songyao Jiang, Joseph P. Robinson, Yun Fu
Face frontalization provides an effective and efficient way for face data augmentation and further improves the face recognition performance in extreme pose scenario.
2 code implementations • 15 Feb 2020 • Joseph P. Robinson, Yu Yin, Zaid Khan, Ming Shao, Siyu Xia, Michael Stopa, Samson Timoner, Matthew A. Turk, Rama Chellappa, Yun Fu
Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much higher than ever before.
1 code implementation • 6 Feb 2020 • Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain.
1 code implementation • 19 Nov 2019 • Yu Yin, Joseph P. Robinson, Yulun Zhang, Yun Fu
As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.
1 code implementation • 23 Aug 2019 • Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, Shijin Wang
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.
1 code implementation • 7 Jun 2019 • Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, Guoping Hu
In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.
no code implementations • 27 May 2019 • Yu Yin, Zhenya Huang, Enhong Chen, Qi Liu, Fuzheng Zhang, Xing Xie, Guoping Hu
Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly.
no code implementations • 27 May 2019 • Yu Yin, Qi Liu, Zhenya Huang, Enhong Chen, Wei Tong, Shijin Wang, Yu Su
Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.
1 code implementation • 18 Nov 2018 • Yu Yin, Mohsen Nabian, Miolin Fan, Chun-An Chou, Maria Gendron, Sarah Ostadabbas
In this paper, we present a multimodal approach to simultaneously analyze facial movements and several peripheral physiological signals to decode individualized affective experiences under positive and negative emotional contexts, while considering their personalized resting dynamics.
1 code implementation • 3 Nov 2017 • Shuangjun Liu, Yu Yin, Sarah Ostadabbas
Using the HOG rectification method, the pose estimation performance of CPM significantly improved by 26. 4% in PCK0. 1 criteria compared to the model without such rectification.