1 code implementation • 18 Feb 2024 • Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, Tieniu Tan
In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects.
no code implementations • 18 Feb 2024 • Jinghao Zhang, YuTing Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
no code implementations • 1 Feb 2024 • Qilong Yan, Yufeng Zhang, Jinghao Zhang, Jingpu Duan, Jian Yin
This could lead the meta-learner to face complex tasks too soon, hindering proper learning.
no code implementations • 1 Aug 2023 • Jinghao Zhang, Feng Zhao
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications.
no code implementations • 25 Jun 2023 • Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang
Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities.
1 code implementation • CVPR 2023 • Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, Feng Zhao
Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
no code implementations • CVPR 2023 • Zizheng Yang, Jie Huang, Jiahao Chang, Man Zhou, Hu Yu, Jinghao Zhang, Feng Zhao
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
1 code implementation • 1 Nov 2021 • Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
1 code implementation • 19 Apr 2021 • Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang
To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.
no code implementations • 4 Mar 2021 • Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
1 code implementation • 28 Jan 2021 • Yufeng Zhang, Jinghao Zhang, Zeyu Cui, Shu Wu, Liang Wang
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial.