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Found 8 papers, 3 papers with code
PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments.
SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method
Data augmentation is a prevalent technique for improving performance in various machine learning applications.
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks
Designing powerful adversarial attacks is of paramount importance for the evaluation of $\ell_p$-bounded adversarial defenses.
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations.
Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss
Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity.
MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis
Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs (early fusion).
Ranked #7 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(using extra training data)
AudioVisual Speech Synthesis: A brief literature review
This brief literature review studies the problem of audiovisual speech synthesis, which is the problem of generating an animated talking head given a text as input.
End-to-end Generative Zero-shot Learning via Few-shot Learning
In this work, we introduce Z2FSL, an end-to-end generative ZSL framework that uses such an approach as a backbone and feeds its synthesized output to a Few-Shot Learning (FSL) algorithm.
