CoCLR is a self-supervised learning method for video representation that leverages visual-only data. It improves video representation models without the need for labeled data.
CoCLR is a self-supervised learning method for video representation that leverages visual-only data. It improves video representation models without the need for labeled data.
CoCLR is a novel self-supervised learning method for video representation. It exploits visual-only data to co-train video representation models using InfoNCE objective and MoCo on videos. This method addresses the need to process large amounts of unlabeled video data effectively, making it valuable for applications where labeled data is scarce or unavailable.
Who will use Supervised app?
Researchers in video representation learning
Data scientists working with video data
Developers of machine learning models
Video content analysis experts
How to use the Supervised app?
Step1: Gather your unlabeled video data
Step2: Implement the CoCLR method using the provided repository
Step3: Train your video representation model using CoCLR
Step4: Evaluate the model performance using standard metrics