Search Results for author: Yuhang Wu

Found 24 papers, 4 papers with code

AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models

no code implementations13 Jun 2024 Yuhang Wu, Wenmeng Yu, Yean Cheng, Yan Wang, Xiaohan Zhang, Jiazheng Xu, Ming Ding, Yuxiao Dong

Evaluating the alignment capabilities of large Vision-Language Models (VLMs) is essential for determining their effectiveness as helpful assistants.


Large Language Model Enhanced Machine Learning Estimators for Classification

1 code implementation8 May 2024 Yuhang Wu, Yingfei Wang, Chu Wang, Zeyu Zheng

Pre-trained large language models (LLM) have emerged as a powerful tool for simulating various scenarios and generating output given specific instructions and multimodal input.

Binary Classification Language Modelling +2

OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction

no code implementations28 Feb 2024 Jian Liu, Sipeng Zhang, Chuixin Kong, Wenyuan Zhang, Yuhang Wu, Yikang Ding, Borun Xu, Ruibo Ming, Donglai Wei, Xianming Liu

This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023.

Autonomous Driving Data Augmentation

Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation

no code implementations29 Jan 2024 Jiaqi Wang, Yuzhong Chen, Yuhang Wu, Mahashweta Das, Hao Yang, Fenglong Ma

Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side.

Clustering Personalized Federated Learning

Assessing Prompt Injection Risks in 200+ Custom GPTs

1 code implementation20 Nov 2023 Jiahao Yu, Yuhang Wu, Dong Shu, Mingyu Jin, Sabrina Yang, Xinyu Xing

In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications.

Causal inference with Machine Learning-Based Covariate Representation

no code implementations3 Nov 2023 Yuhang Wu, Jinghai He, Zeyu Zheng

Utilizing covariate information has been a powerful approach to improve the efficiency and accuracy for causal inference, which support massive amount of randomized experiments run on data-driven enterprises.

Causal Inference

Best Arm Identification with Fairness Constraints on Subpopulations

no code implementations8 Apr 2023 Yuhang Wu, Zeyu Zheng, Tingyu Zhu

The BAICS problem aims at correctly identify, with high confidence, the arm with the largest expected reward from all arms that satisfy subpopulation constraints.


Adaptive Data Fusion for Multi-task Non-smooth Optimization

no code implementations22 Oct 2022 Henry Lam, Kaizheng Wang, Yuhang Wu, Yichen Zhang

We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management.

Decision Making Management

Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

no code implementations22 Dec 2021 Yuhang Wu, Tengteng Huang, Haotian Yao, Chi Zhang, Yuanjie Shao, Chuchu Han, Changxin Gao, Nong Sang

First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain.

Contrastive Learning Domain Adaptive Person Re-Identification +2

Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection

no code implementations15 Aug 2021 Yuhang Wu, Mengting Gu, Lan Wang, Yusan Lin, Fei Wang, Hao Yang

Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data.

Anomaly Detection Time Series +1

Adversarial Example Detection Using Latent Neighborhood Graph

no code implementations ICCV 2021 Ahmed Abusnaina, Yuhang Wu, Sunpreet Arora, Yizhen Wang, Fei Wang, Hao Yang, David Mohaisen

We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an input example to determine if the input example is adversarial.

Adversarial Attack Graph Attention

Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions

no code implementations31 Dec 2020 Yuhang Wu, Sunpreet S. Arora, Yanhong Wu, Hao Yang

Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers.

GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

1 code implementation5 Jun 2020 Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei zhang, Hao Yang, Hari Sundaram

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.

Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study

no code implementations24 Mar 2020 Dinh-Luan Nguyen, Sunpreet S. Arora, Yuhang Wu, Hao Yang

While feasible, digital attacks have limited applicability in attacking deployed systems, including face recognition systems, where an adversary typically has access to the input and not the transmission channel.

Face Recognition

Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition

no code implementations12 Mar 2019 Yuhang Wu, Ioannis A. Kakadiaris

The compact face representation is not sensitive to the number of patches that are used to construct the facial template and is more suitable for incorporating the information from different view angles for image-set based face recognition.

Face Recognition Face Verification +1

Convolutional Point-set Representation: A Convolutional Bridge Between a Densely Annotated Image and 3D Face Alignment

no code implementations17 Mar 2018 Yuhang Wu, Le Anh Vu Ha, Xiang Xu, Ioannis A. Kakadiaris

The method relies on Convolutional Point-set Representation (CPR), a carefully designed matrix representation to summarize different layers of information encoded in the set of detected points in the annotated image.

3D Face Alignment Face Alignment

Facial 3D Model Registration Under Occlusions With SensiblePoints-based Reinforced Hypothesis Refinement

no code implementations2 Sep 2017 Yuhang Wu, Ioannis A. Kakadiaris

The visual clues extracted from the fiducial points, non-fiducial points, and facial contour are jointly employed to verify the hypotheses.

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