PyTorch implementation of a collections of scalable Video Transformer Benchmarks.
TianChi AIEarth Contest Solution
Experimental fork of TimeSformer from Facebook AI to extend the attention-based model to video generation.
A research implementation of a context-conditioned, zero-shot video anomaly detection framework that integrates spatiotemporal features extracted via TimeSformer with contrastive predictive coding and semantic alignment through CLIP. The repository includes training and evaluation pipelines, configuration files, and the accompanying thesis/paper.
This app allows you to upload a video, converts it into frames, and predicts the action using a pre-trained model. We use TimeSformer, a state-of-the-art video transformer model, which processes video frames as a sequence of images and captures temporal relationships to predict actions effectively. Experience seamless action recognition with visual
Extended implementation of the Vesuvius Challenge 2023 Grand Prize winner. This project features extra functionality compared to the original ink detection script, giving users fine control over the layers they perform inference on.
Experimental fork of TimeSformer from Facebook AI to extend the attention-based model to video generation.
This app allows you to upload a video, converts it into frames, and predicts the action using a pre-trained model. We use TimeSformer, a state-of-the-art video transformer model, which processes video frames as a sequence of images and captures temporal relationships to predict actions effectively. Experience seamless action recognition with visual
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