Search Results for author: Mengze Li

Found 12 papers, 4 papers with code

Generalization Gap in Data Augmentation: Insights from Illumination

no code implementations11 Apr 2024 Jianqiang Xiao, Weiwen Guo, Junfeng Liu, Mengze Li

In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques.

Data Augmentation

Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence

1 code implementation14 Jan 2024 Zhengqing Fang, Shuowen Zhou, Zhouhang Yuan, Yuxuan Si, Mengze Li, Jinxu Li, Yesheng Xu, Wenjia Xie, Kun Kuang, Yingming Li, Fei Wu, Yu-Feng Yao

This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness.

Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer

no code implementations21 Nov 2023 Wenqiao Zhang, Zheqi Lv, Hao Zhou, Jia-Wei Liu, Juncheng Li, Mengze Li, Siliang Tang, Yueting Zhuang

Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources.

Domain Adaptation Transfer Learning

Panoptic Scene Graph Generation with Semantics-Prototype Learning

1 code implementation28 Jul 2023 Li Li, Wei Ji, Yiming Wu, Mengze Li, You Qin, Lina Wei, Roger Zimmermann

To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.

Graph Generation Panoptic Scene Graph Generation

Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models

no code implementations ICCV 2023 Juncheng Li, Minghe Gao, Longhui Wei, Siliang Tang, Wenqiao Zhang, Mengze Li, Wei Ji, Qi Tian, Tat-Seng Chua, Yueting Zhuang

Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models.

Domain Generalization Few-Shot Learning +1

IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System

no code implementations14 Feb 2023 Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, Fei Wu

The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication.

Recommendation Systems Vocal Bursts Intensity Prediction

Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning

1 code implementation CVPR 2023 Wei Ji, Renjie Liang, Zhedong Zheng, Wenqiao Zhang, Shengyu Zhang, Juncheng Li, Mengze Li, Tat-Seng Chua

Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection.

Active Learning Moment Retrieval +1

WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding

no code implementations CVPR 2023 Mengze Li, Han Wang, Wenqiao Zhang, Jiaxu Miao, Zhou Zhao, Shengyu Zhang, Wei Ji, Fei Wu

WINNER first builds the language decomposition tree in a bottom-up manner, upon which the structural attention mechanism and top-down feature backtracking jointly build a multi-modal decomposition tree, permitting a hierarchical understanding of unstructured videos.

Contrastive Learning Spatio-Temporal Video Grounding +1

BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval

no code implementations9 Jul 2022 Wenqiao Zhang, Jiannan Guo, Mengze Li, Haochen Shi, Shengyu Zhang, Juncheng Li, Siliang Tang, Yueting Zhuang

In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image.

Content-Based Image Retrieval counterfactual +2

Enhancing Fairness of Visual Attribute Predictors

1 code implementation7 Jul 2022 Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Mengze Li, Erdem Ünal, Abouzar Eslami, Stefan Gumhold

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.

Attribute Fairness

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

no code implementations ACL 2022 Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, ShiLiang Pu, Fei Wu

To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.

Descriptive Representation Learning +1

Geometry of the Minimum Volume Confidence Sets

no code implementations16 Feb 2022 Heguang Lin, Mengze Li, Daniel Pimentel-Alarcón, Matthew Malloy

Prior work showed the minimum-volume confidence sets are the level-sets of a discontinuous function defined by an exact p-value.

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