Search Results for author: Yin Zhao

Found 15 papers, 3 papers with code

Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model

no code implementations27 Feb 2024 Panqi Jia, A. Burakhan Koyuncu, Jue Mao, Ze Cui, Yi Ma, Tiansheng Guo, Timofey Solovyev, Alexander Karabutov, Yin Zhao, Jing Wang, Elena Alshina, Andre Kaup

To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed.

Image Compression

Triple Disentangled Representation Learning for Multimodal Affective Analysis

no code implementations29 Jan 2024 Ying Zhou, Xuefeng Liang, Han Chen, Yin Zhao

We revisit the disentanglement issue, and propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data.

Disentanglement

Learning Subjective Time-Series Data via Utopia Label Distribution Approximation

no code implementations15 Jul 2023 Wenxin Xu, Hexin Jiang, Xuefeng Liang, Ying Zhou, Yin Zhao, Jie Zhang

In this work, we propose Utopia Label Distribution Approximation (ULDA) for time-series data, which makes the training label distribution closer to real-world but unknown (utopia) label distribution.

Age Estimation Depth Estimation +4

Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment

1 code implementation NeurIPS 2021 Yin Zhao, Minquan Wang, Longjun Cai

Eliminating the covariate shift cross domains is one of the common methods to deal with the issue of domain shift in visual unsupervised domain adaptation.

Unsupervised Domain Adaptation

Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark

1 code implementation23 Sep 2021 Xun Gao, Yin Zhao, Jie Zhang, Longjun Cai

We expect the ERATO as well as our proposed SMTA to open up a new way for PERR task in video understanding and further improve the research of multi-modal fusion methodology.

Video Understanding

Subjective evaluation of traditional and learning-based image coding methods

no code implementations28 Jul 2021 Zhigao Fang, JiaQi Zhang, Lu Yu, Yin Zhao

Additionally, we utilize some typical and frequently used objective quality metrics to evaluate the coding methods in the experiment as comparison.

RNE: A Scalable Network Embedding for Billion-scale Recommendation

no code implementations10 Mar 2020 Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, Yuan Qi

However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.

Network Embedding

VIDEO AFFECTIVE IMPACT PREDICTION WITH MULTIMODAL FUSION AND LONG-SHORT TEMPORAL CONTEXT

no code implementations25 Sep 2019 Yin Zhao, Longjun Cai, Chaoping Tu, Jie Zhang, Wu Wei

Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but have not been successfully exploited.

Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context

no code implementations1 Sep 2019 Jie Zhang, Yin Zhao, Longjun Cai, Chaoping Tu, Wu Wei

We select the most suitable modalities for valence and arousal tasks respectively and each modal feature is extracted using the modality-specific pre-trained deep model on large generic dataset.

PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation

1 code implementation IJCAI 2019 Qiong Wu, Yong liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan

This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.

Recommendation Systems

Recent Advances in Diversified Recommendation

no code implementations16 May 2019 Qiong Wu, Yong liu, Chunyan Miao, Yin Zhao, Lu Guan, Haihong Tang

With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.

Recommendation Systems

Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation

no code implementations19 Mar 2019 Yong Liu, Yinan Zhang, Qiong Wu, Chunyan Miao, Lizhen Cui, Binqiang Zhao, Yin Zhao, Lu Guan

Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.

Recommendation Systems reinforcement-learning +1

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