activity
20182026
most citedDo I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines

2 citations · 9 across the 14 of their papers we have counts for

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Showing cs.MMShow all

7 papers · 1 filter

cs.MM2024★ 2 cited

Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome

Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun +4

Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rel…

cs.MM2023

TV-watching partner robot: Analysis of User's Experience

Donghuo Zeng, Jianming Wu, Gen Hattori +1

Watching TV not only provides news information but also gives an opportunity for different generations to communicate. With the proliferation of smartphones, PC, and the Internet,…

cs.MM2022

Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval

Donghuo Zeng, Yanan Wang, Jianming Wu +1

The heterogeneity gap problem is the main challenge in cross-modal retrieval. Because cross-modal data (e.g. audiovisual) have different distributions and representations that cann…

cs.MM2021★ 2 cited

Learning Explicit and Implicit Latent Common Spaces for Audio-Visual Cross-Modal Retrieval

Donghuo Zeng, Jianming Wu, Gen Hattori +2

Learning common subspace is prevalent way in cross-modal retrieval to solve the problem of data from different modalities having inconsistent distributions and representations that…

cs.MM2021

SHECS: A Local Smart Hands-free Elderly Care Support System on Smart AR Glasses with AI Technology

Donghuo Zeng, Jianming Wu, Bo Yang +6

Some elderly care homes attempt to remedy the shortage of skilled caregivers and provide long-term care for the elderly residents, by enhancing the management of the care support s…

cs.MM2019

Audio-Visual Embedding for Cross-Modal MusicVideo Retrieval through Supervised Deep CCA

Donghuo Zeng, Yi Yu, Keizo Oyama

Deep learning has successfully shown excellent performance in learning joint representations between different data modalities. Unfortunately, little research focuses on cross-moda…