What is Computer Vision

Some companies will use computer vision as part of their content moderation practices. This is one approach to content moderation that can assist with scale and maintain a user-friendly environment.  

So, what exactly is computer vision?

Computer vision is a field of artificial intelligence and computer science that focuses on enabling computers to interpret and understand visual information from the world. The goal of computer vision is to enable machines to replicate & improve upon the human ability to perceive and make deicsions based on visual data. Computer vision applications are widespread and can be found in various industries including content moderation.

Some companies will use computer vision as it enables automated analysis and understanding of visual content, allowing companies to enforce community guidelines. Here are some of the ways that computer vision ties into content moderation:

•  Image & video analysis

Computer vision algorithms can scan images and videos for specific visual elements or patterns that may violate content guidelines. For example, it can identify nudity, violence, or other explicit content.

• Object detection

Computer vision techniques, including object detection models, can identify objects or entities within images. This is useful for recognizing specific items, logos, or symbols that might be associated with prohibited content.

• Contextual understanding

Advanced computer vision models can be trained to understand the context in which visual content appears. This helps in distinguishing between content that is innocuous in a certain context and content that is genuinely objectionable.

• Facial recognition

Computer vision can be employed for facial recognition to identify individuals in images or videos. This can assist in enforcing rules related to privacy or identifying potentially harmful users.

• Moderation automation

By leveraging machine learning and computer vision models, platforms can automate the initial stages of content moderation. This allows for the quick identification and filtering of inappropriate content, reducing the burden on human moderators.

• Real-time monitoring

By leveraging machine learning and computer vision models, platforms can automate the initial stages of content moderation. This allows for the quick identification and filtering of inappropriate content, reducing the burden on human moderators.

• User authentication

Facial recognition and other biometric analysis through computer vision can be used for user authentication, helping platforms verify the identity of users and prevent the creation of fake accounts.

Whilst computer vision can greatly enhance the efficiency of content moderation, it is important to note that it is not flawless and that false positives/negatives can occur. As a result, many companies will choose to combine other tools and tactics as well as using human moderators. This helps to ensure a more nuanced and accurate content review process.