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Found 13 papers, 2 papers with code
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.
Anchor-aware Deep Metric Learning for Audio-visual Retrieval
However, the model training fails to fully explore the space due to the scarcity of training data points, resulting in an incomplete representation of the overall positive and negative distributions.
Two-Stage Triplet Loss Training with Curriculum Augmentation for Audio-Visual Retrieval
We propose a two-stage training paradigm that guides the model's learning process from semi-hard to hard triplets.
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning
In this work, to achieve high efficiency-performance multimodal transfer learning, we propose VideoAdviser, a video knowledge distillation method to transfer multimodal knowledge of video-enhanced prompts from a multimodal fundamental model (teacher) to a specific modal fundamental model (student).
Topic-switch adapted Japanese Dialogue System based on PLATO-2
Furthermore, our proposed topic-switch algorithm achieves an average score of 1. 767 and outperforms PLATO-JDS by 0. 267, indicating its effectiveness in improving the user experience of our system.
Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines
The experiments show the usefulness of the proposed dataset.
Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval
In this paper, to reduce the interference of hard negative samples in representation learning, we propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss.
MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio
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).
Unsupervised Generative Adversarial Alignment Representation for Sheet music, Audio and Lyrics
In this paper, we propose an unsupervised generative adversarial alignment representation (UGAAR) model to learn deep discriminative representations shared across three major musical modalities: sheet music, lyrics, and audio, where a deep neural network based architecture on three branches is jointly trained.
Learning Joint Embedding for Cross-Modal Retrieval
A cross-modal retrieval process is to use a query in one modality to obtain relevant data in another modality.
Audio-Visual Embedding for Cross-Modal MusicVideo Retrieval through Supervised Deep CCA
ii) We propose an end-to-end deep model for cross-modal audio-visual learning where S-DCCA is trained to learn the semantic correlation between audio and visual modalities.
Personalized Music Recommendation with Triplet Network
Since many online music services emerged in recent years so that effective music recommendation systems are desirable.
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
In particular, two significant contributions are made: i) a better representation by constructing deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace.
