Genre classification
46 papers with code • 2 benchmarks • 6 datasets
Genre classification is the process of grouping objects together based on defined similarities such as shape, pixel, location, or intensity.
FMA
MuMu
Moviescope
Trailers12k
PTVD
Latest papers
BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification
dminus1/genre
•
•
While performance of many text classification tasks has been recently improved due to Pre-trained Language Models (PLMs), in this paper we show that they still suffer from a performance gap when the underlying distribution of topics changes.
Pre-training Music Classification Models via Music Source Separation
FaceOnLive/Spleeter-Android-iOS
•
•
In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks.
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning
aoluming/IDKG
•
•
Firstly we retrieve the relevant embedding from the knowledge graph by utilizing group relations in metadata and then integrate it with other modalities.
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
palchenli/TVD
•
•
Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently.
Multi-Source Contrastive Learning from Musical Audio
Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated.
Deep Architectures for Content Moderation and Movie Content Rating
Rating a video based on its content is an important step for classifying video age categories.
Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block
JNAIC/PIPMN
•
•
Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification.
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
wngh1187/ipet
•
•
To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
Hierarchical quantum circuit representations for neural architecture search
The QCNN is a circuit model inspired by the architecture of Convolutional Neural Networks (CNNs).
