Enhance video understanding with Amazon Bedrock Data Automation and open-set object detection

In real-world video and image analysis, businesses often face the challenge of detecting objects that weren’t part of a model’s original training set. This becomes especially difficult in dynamic environments where new, unknown, or user-defined objects frequently appear. For example, media publishers might want to track emerging brands or products in user-generated content; advertisers need to analyze product appearances in influencer videos despite visual variations; retail providers aim to support flexible, descriptive search; self-driving cars must identify unexpected road debris; and manufacturing systems need to catch novel or subtle defects without prior labeling.In all these cases, traditional closed-set object detection (CSOD) models—which only recognize a fixed list of predefined categories—fail to deliver. They either misclassify the unknown objects or ignore them entirely, limiting their usefulness for real-world applications.Open-set object detection (OSOD) is an approach that enables models to detect both known and previously unseen ...