1 code implementation • 29 Mar 2024 • You Wu, Kean Liu, Xiaoyue Mi, Fan Tang, Juan Cao, Jintao Li
Extensive experiments on various kinds of visual attributes with SOTA personalization methods show the ability of the proposed method to mimic target visual appearance in novel contexts, thus improving the controllability and flexibility of personalization.
1 code implementation • 28 Mar 2024 • Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang, Oliver Deussen, WeiMing Dong, Jintao Li, Tong-Yee Lee
Based on the adapters broken apart for separate training content and style, we then make the entity parameter space by reconstructing the content and style PLPs matrices, followed by fine-tuning the combined adapter to generate the target object with the desired appearance.
1 code implementation • 25 Mar 2024 • Ziyao Huang, Fan Tang, Yong Zhang, Xiaodong Cun, Juan Cao, Jintao Li, Tong-Yee Lee
We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances.
no code implementations • 8 Jan 2024 • Shulin Zeng, Jun Liu, Guohao Dai, Xinhao Yang, Tianyu Fu, Hongyi Wang, Wenheng Ma, Hanbo Sun, Shiyao Li, Zixiao Huang, Yadong Dai, Jintao Li, Zehao Wang, Ruoyu Zhang, Kairui Wen, Xuefei Ning, Yu Wang
However, existing GPU and transformer-based accelerators cannot efficiently process compressed LLMs, due to the following unresolved challenges: low computational efficiency, underutilized memory bandwidth, and large compilation overheads.
no code implementations • 16 Oct 2023 • Qiong Nan, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Guang Yang, Jintao Li, Kai Shu
To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e. g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts.
2 code implementations • 7 Feb 2023 • Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, Jintao Li
With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem.
no code implementations • COLING 2022 • Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li
To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains.
no code implementations • 1 Sep 2022 • Guang Yang, Wu Liu, Xinchen Liu, Xiaoyan Gu, Juan Cao, Jintao Li
To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos.
1 code implementation • 6 May 2022 • Qiang Sheng, Juan Cao, H. Russell Bernard, Kai Shu, Jintao Li, Huan Liu
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.
no code implementations • 21 Mar 2022 • Yuting Yang, Pei Huang, Juan Cao, Jintao Li, Yun Lin, Jin Song Dong, Feifei Ma, Jian Zhang
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
1 code implementation • 17 Mar 2022 • Guang Yang, Juan Cao, Qiang Sheng, Peng Qi, Xirong Li, Jintao Li
However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.
no code implementations • 15 Jan 2022 • Yuting Yang, Wenqiang Lei, Pei Huang, Juan Cao, Jintao Li, Tat-Seng Chua
In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST.
no code implementations • 11 Jan 2022 • Yuting Yang, Pei Huang, Feifei Ma, Juan Cao, Meishan Zhang, Jian Zhang, Jintao Li
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
1 code implementation • 4 Jan 2022 • Qiong Nan, Juan Cao, Yongchun Zhu, Yanyan Wang, Jintao Li
In this paper, we first design a benchmark of fake news dataset for MFND with domain label annotated, namely Weibo21, which consists of 4, 488 fake news and 4, 640 real news from 9 different domains.
no code implementations • 8 Oct 2021 • Paolo D'Alberto, Jiangsha Ma, Jintao Li, Yiming Hu, Manasa Bollavaram, Shaoxia Fang
We have a FPGA design, we make it fast, efficient, and tested for a few important examples.
no code implementations • 14 Dec 2020 • Jintao Li, Simon K. Schnyder, Matthew S. Turner, Ryoichi Yamamoto
We develop a simple model of the cell cycle, the fundamental regulatory network controlling growth and division, and couple this to the physical forces arising within the cell collective.
Biological Physics Soft Condensed Matter
2 code implementations • CVPR 2020 • Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng
Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution.
no code implementations • 25 Dec 2019 • Yu Li, Sheng Tang, Rui Zhang, Yongdong Zhang, Jintao Li, Shuicheng Yan
While in situations where two domains are asymmetric in complexity, i. e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity.
no code implementations • 13 Aug 2019 • Peng Qi, Juan Cao, Tianyun Yang, Junbo Guo, Jintao Li
In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively.
no code implementations • 2 Feb 2019 • Yuting Yang, Juan Cao, Mingyan Lu, Jintao Li, Chia-Wen Lin
SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to.
no code implementations • 12 Dec 2018 • Tianyi Wu, Sheng Tang, Rui Zhang, Juan Cao, Jintao Li
Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.
2 code implementations • 7 Nov 2018 • Rui Zhang, Sheng Tang, Yu Li, Junbo Guo, Yongdong Zhang, Jintao Li, Shuicheng Yan
The S3-GAN consists of an encoder network, a generator network, and an adversarial network.
1 code implementation • 12 Dec 2017 • Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, Tao Mei
With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space.
no code implementations • ICCV 2017 • Rui Zhang, Sheng Tang, Yongdong Zhang, Jintao Li, Shuicheng Yan
Through adding a new scale regression layer, we can dynamically infer the position-adaptive scale coefficients which are adopted to resize the convolutional patches.
no code implementations • 4 Jul 2017 • Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, Qi Tian
Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR).
no code implementations • 4 Jul 2017 • Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, Qi Tian
The representation learning risk is evaluated by the proposed part loss, which automatically generates several parts for an image, and computes the person classification loss on each part separately.
Ranked #97 on Person Re-Identification on Market-1501
no code implementations • CVPR 2017 • Xishan Zhang, Ke Gao, Yongdong Zhang, Dongming Zhang, Jintao Li, Qi Tian
This paper contributes to: 1)The first in-depth study of the weakness inherent in data-driven static fusion methods for video captioning.