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CrossCLR

CrossCLR is a Python implementation of a cross-modal contrastive-learning method for training multimodal video representations. Its loss function accepts video and text feature tensors and returns a training loss.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

CrossCLR — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use CrossCLR when an existing Python workflow can supply video and text feature tensors and you need the documented loss component for representation-learning experiments.

Who it is for

  • Researchers developing multimodal video representations.
  • Machine-learning engineers experimenting with video-text retrieval and contrastive training.

Documented uses

  • Compute a contrastive training loss from video and text features.
  • Incorporate the CrossCLR loss into a video-text representation training pipeline.
  • Prototype or compare contrastive-learning setups for video-text retrieval.
Before you choose

Requirements & limits

The public repository specifies an Apache-2.0 license.

  • The documented loss interface expects already-computed video and text features shaped [bsz, f_dim].
  • The README demonstrates loss calculation, not a complete training or retrieval pipeline.
  • The implementation is Python-based and licensed under Apache-2.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Calculate CrossCLR loss from video and text features

Use the documented loss interface to obtain a contrastive training loss from compatible video and text feature tensors.

Before you start

  • Python project with the CrossCLR repository available.
  • Video and text feature tensors shaped [bsz, f_dim].
  • Temperature and negative-sample weight values for configuring the loss.

Steps

  1. Import CrossCLR_onlyIntraModality from trainer.loss.

  2. Initialize it with temperature and negative_weight arguments.

  3. Pass video_features and text_features to the criterion to obtain the loss.

Expected outcome

A loss value computed from the supplied video and text features.

Guide limitations

  • The README does not specify feature extraction, dataset preparation, optimizer setup, or a complete training loop.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
amazon-science/crossmodal-contrastive-learning · Official homepage title · retained in its source language
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