Event Segmentation
7 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Event Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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Kinetics
Kinetics 400
EgoProceL
Most implemented papers
Generic Event Boundary Detection: A Benchmark for Event Segmentation
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.
A Perceptual Prediction Framework for Self Supervised Event Segmentation
CVPRUSFTampa/EventSegmentation
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CVPR 2019
We also show that the proposed approach is able to learn highly discriminative features that help improve action recognition when used in a representation learning paradigm.
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution.
Enhancing temporal segmentation by nonlocal self-similarity
mdimiccoli/Nonlocal-self-similarity-1D
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Temporal segmentation of untrimmed videos and photo-streams is currently an active area of research in computer vision and image processing.
Generic Event Boundary Detection Challenge at CVPR 2021 Technical Report: Cascaded Temporal Attention Network (CASTANET)
In this work, we design a Cascaded Temporal Attention Network (CASTANET) for GEBD, which is formed by three parts, the backbone network, the temporal attention module, and the classification module.
CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation
wang3702/CoSeg
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Unlike the mainstream clustering-based methods, our framework exploits a transformer-based feature reconstruction scheme to detect event boundary by reconstruction errors.
Developing hierarchical anticipations via neural network-based event segmentation
cognitivemodeling/hierarchicalgatel0rd
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Humans can make predictions on various time scales and hierarchical levels.
