The latest research indicates that TESTA is a method designed to expedite the comprehension of lengthy videos by combining similar frames and patches. The introduction of this approach has successfully reduced computational load and enhanced the performance of matching segments to videos and answering questions about long videos. By identifying similar frames and utilizing patches, TESTA significantly improves video understanding efficiency, providing a faster and more economical solution for large-scale video comprehension tasks. This method also introduces efficient token aggregation and pre-trained video-language models, enhancing the understanding of video content and offering more opportunities for innovation and performance improvement for researchers, developers, and organizations.