1 code implementation • 30 Mar 2024 • Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, BoWen Zhou, Jufeng Yang
Camouflaged vision perception is an important vision task with numerous practical applications.
no code implementations • 28 Nov 2023 • Shupeng Cheng, Ge-Peng Ji, Pengda Qin, Deng-Ping Fan, BoWen Zhou, Peng Xu
Our motivation is to make full use of the semantic intelligence and intrinsic knowledge of recent Multimodal Large Language Models (MLLMs) to decompose this complex task in a human-like way.
1 code implementation • ICCV 2023 • Chaorui Deng, Qi Chen, Pengda Qin, Da Chen, Qi Wu
In text-video retrieval, recent works have benefited from the powerful learning capabilities of pre-trained text-image foundation models (e. g., CLIP) by adapting them to the video domain.
no code implementations • ICCV 2023 • Zi Qian, Xin Wang, Xuguang Duan, Pengda Qin, Yuhong Li, Wenwu Zhu
Based on our formulation, we further propose MulTi-Modal PRompt LearnIng with DecouPLing bEfore InTeraction (TRIPLET), a novel approach that builds on a pre-trained vision-language model and consists of decoupled prompts and prompt interaction strategies to capture the complex interactions between modalities.
1 code implementation • 20 Nov 2022 • Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue, Wenhu Chen
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity.
Ranked #1 on Story Visualization on Pororo
no code implementations • 13 Jun 2021 • Runshi Liu, Pengda Qin, Yuhong Li, Weigao Wen, Dong Li, Kefeng Deng, Qiang Wu
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
no code implementations • 3 Jun 2021 • Pengda Qin, Yuhong Li, Kefeng Deng, Qiang Wu
Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly.
2 code implementations • 8 Jan 2020 • Pengda Qin, Xin Wang, Wenhu Chen, Chunyun Zhang, Weiran Xu, William Yang Wang
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
no code implementations • IJCNLP 2019 • Siyao Li, Deren Lei, Pengda Qin, William Yang Wang
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues.
no code implementations • 15 Aug 2019 • Shaolei Wang, Wanxiang Che, Qi Liu, Pengda Qin, Ting Liu, William Yang Wang
The pre-trained network is then fine-tuned using human-annotated disfluency detection training data.
2 code implementations • ACL 2019 • Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan, William Yang Wang
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on Data-to-Text Generation on MULTIWOZ 2.1
2 code implementations • ACL 2018 • Pengda Qin, Weiran Xu, William Yang Wang
The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.
no code implementations • ACL 2018 • Pengda Qin, Weiran Xu, William Yang Wang
Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem.