4 citations · 14 across the 20 of their papers we have counts for
21 papers
Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation
Donghuo Zeng, Roberto Legaspi, Kazushi Ikeda
Effective persuasive dialogue agents adapt their strategies to individual users, accounting for the evolution of their psychological states and intentions throughout conversations.…
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypotheti…
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This…
Metric Learning with Progressive Self-Distillation for Audio-Visual Embedding Learning
Donghuo Zeng, Kazushi Ikeda
Metric learning projects samples into an embedded space, where similarities and dissimilarities are quantified based on their learned representations. However, existing methods oft…
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…
Anchor-aware Deep Metric Learning for Audio-visual Retrieval
Donghuo Zeng, Yanan Wang, Kazushi Ikeda +1
Metric learning minimizes the gap between similar (positive) pairs of data points and increases the separation of dissimilar (negative) pairs, aiming at capturing the underlying da…
Two-Stage Triplet Loss Training with Curriculum Augmentation for Audio-Visual Retrieval
Donghuo Zeng, Kazushi Ikeda
The cross-modal retrieval model leverages the potential of triple loss optimization to learn robust embedding spaces. However, existing methods often train these models in a singul…
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning
Yanan Wang, Donghuo Zeng, Shinya Wada +1
Multimodal transfer learning aims to transform pretrained representations of diverse modalities into a common domain space for effective multimodal fusion. However, conventional sy…
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,…
Topic-switch adapted Japanese Dialogue System based on PLATO-2
Donghuo Zeng, Jianming Wu, Yanan Wang +3
Large-scale open-domain dialogue systems such as PLATO-2 have achieved state-of-the-art scores in both English and Chinese. However, little work explores whether such dialogue syst…
Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines
Monisha Singh, Ximi Hoque, Donghuo Zeng +3
The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as `user engagement'. Engagement comprises…
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…
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…
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…
MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio
Donghuo Zeng, Yi Yu, Keizo Oyama
This work present a music dataset named MusicTM-Dataset, which is utilized in improving the representation learning ability of different types of cross-modal retrieval (CMR). Littl…
Unsupervised Generative Adversarial Alignment Representation for Sheet music, Audio and Lyrics
Donghuo Zeng, Yi Yu, Keizo Oyama
Sheet music, audio, and lyrics are three main modalities during writing a song. In this paper, we propose an unsupervised generative adversarial alignment representation (UGAAR) mo…
Learning Joint Embedding for Cross-Modal Retrieval
Donghuo Zeng
A cross-modal retrieval process is to use a query in one modality to obtain relevant data in another modality. The challenging issue of cross-modal retrieval lies in bridging the h…
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…
Personalized Music Recommendation with Triplet Network
Haoting Liang, Donghuo Zeng, Yi Yu +1
Since many online music services emerged in recent years so that effective music recommendation systems are desirable. Some common problems in recommendation system like feature re…
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
Donghuo Zeng, Yi Yu, Keizo Oyama
Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been a very interesting research issue in the field of multimedia, information…
Deep Learning of Human Perception in Audio Event Classification
Yi Yu, Samuel Beuret, Donghuo Zeng +1
In this paper, we introduce our recent studies on human perception in audio event classification by different deep learning models. In particular, the pre-trained model VGGish is u…