Event Detection
217 papers with code • 2 benchmarks • 7 datasets
Libraries
Use these libraries to find Event Detection models and implementationsMost implemented papers
CNN Architectures for Large-Scale Audio Classification
Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio.
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos.
What Makes Training Multi-Modal Classification Networks Hard?
Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart.
WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions.
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs).
Adaptive pooling operators for weakly labeled sound event detection
In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality.
Literary Event Detection
In this work we present a new dataset of literary events{---}events that are depicted as taking place within the imagined space of a novel.
Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
Social media is becoming a primary medium to discuss what is happening around the world.
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.