Search Results for author: Hao Gu

Found 6 papers, 4 papers with code

GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition

1 code implementation7 Dec 2023 Zheng Lian, Licai Sun, Haiyang Sun, Kang Chen, Zhuofan Wen, Hao Gu, Bin Liu, JianHua Tao

To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.

Facial Emotion Recognition Micro Expression Recognition +3

System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation

no code implementations21 Aug 2022 Xinrui Yan, Jiangyan Yi, Chenglong Wang, JianHua Tao, Junzuo Zhou, Hao Gu, Ruibo Fu

The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious content manipulation.

Face Swapping Speech Synthesis

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

1 code implementation13 Jan 2022 Ruichu Cai, Fengzhu Wu, Zijian Li, Jie Qiao, Wei Chen, Yuexing Hao, Hao Gu

By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method.

counterfactual Counterfactual Reasoning +1

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

1 code implementation14 Nov 2021 Zijian Li, Ruichu Cai, Fengzhu Wu, Sili Zhang, Hao Gu, Yuexing Hao, Yuguang

To achieve this, we firstly formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences.

Sequential Recommendation

SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems

no code implementations9 May 2020 Qiaoan Chen, Hao Gu, Lingling Yi, Yishi Lin, Peng He, Chuan Chen, Yangqiu Song

Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.

Graph Attention Recommendation Systems

Semantic Product Search

1 code implementation1 Jul 2019 Priyanka Nigam, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian, Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, Bing Yin

To address these issues, we train a deep learning model for semantic matching using customer behavior data.

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