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Found 14 papers, 7 papers with code
LanSER: Language-Model Supported Speech Emotion Recognition
Speech emotion recognition (SER) models typically rely on costly human-labeled data for training, making scaling methods to large speech datasets and nuanced emotion taxonomies difficult.
Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers
This technical report presents the modeling approaches used in our submission to the ICML Expressive Vocalizations Workshop & Competition multitask track (ExVo-MultiTask).
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars.
BasisNet: Two-stage Model Synthesis for Efficient Inference
With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80. 0% on ImageNet.
Ranked #661 on
Image Classification
on ImageNet
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task.
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph.
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
This work aims at disentangling the contributions of the `adjectives' and `nouns' in the visual prediction of ANPs.
Multilingual Visual Sentiment Concept Matching
The impact of culture in visual emotion perception has recently captured the attention of multimedia research.
Going Deeper for Multilingual Visual Sentiment Detection
In the original MVSO release, adjective-noun pair (ANP) detectors were trained for the six languages using an AlexNet-styled architecture by fine-tuning from DeepSentiBank.
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections.
Deep Cross Residual Learning for Multitask Visual Recognition
We propose a novel extension of residual learning for deep networks that enables intuitive learning across multiple related tasks using cross-connections called cross-residuals.
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments.
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia.
Robust Object Co-detection
Object co-detection aims at simultaneous detection of objects of the same category from a pool of related images by exploiting consistent visual patterns present in candidate objects in the images.
