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Users can generate videos up to 1080p resolution, up to 20 sec long, and in widescreen, vertical or square aspect ratios. You can bring your own assets to extend, remix, and blend, or generate entirely new content from text.

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We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn. Fifteen years ago, Quiroga et al.1 discovered that the human brain possesses multimodal neurons. These neurons respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the “Halle Berry” neuron, a neuron featured in both Scientific American⁠(opens in a new window) and The New York Times⁠(opens in a new window), that responds to photographs, sketches, and the text “Halle Berry” (but not other names). Two months ago, OpenAI announced CLIP⁠, a general-purpose vision system that matches the performance of a ResNet-50,2 but outperforms existing vision systems on some of the most challenging datasets. Each of these challenge datasets, ObjectNet, ImageNet Rendition, and ImageNet Sketch, stress tests the model’s robustness to not recognizing not just simple distortions or changes in lighting or pose, but also to complete abstraction and reconstruction—sketches, cartoons, and even statues of the objects. Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP. One such neuron, for example, is a “Spider-Man” neuron (bearing a remarkable resemblance to the “Halle Berry” neuron) that responds to an image of a spider, an image of the text “spider,” and the comic book character “Spider-Man” either in costume or illustrated. Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction. We discover that the highest layers of CLIP organize images as a loose semantic collection of ideas, providing a simple explanation for both the model’s versatility and the representation’s compactness.

MoviesonScreen

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Movies On Screen #moviesonscreen is one of the top authorities on movies on YouTube. We upload daily videos and movie theories about Marvel, Avengers, Disney Pixar, Nickelodeon, Family Guy, The Simpsons, and all superhero content. Movies On Screen #moviesonscreen continues to be the go-to source for movie and TV facts, theories, and behind-the-scenes knowledge! For copyright matters relating to our channel please contact us directly at: https://www.instagram.com/nina_drowan/ Stay tuned with us.

"Unifying Visions: Sharing Stories on Common Screen"

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"Common Screen" is a channel that serves as a digital gathering place for individuals from all walks of life. It is a platform that embraces diversity, celebrates inclusivity, and encourages dialogue among its viewers. The channel showcases a wide range of content, including thought-provoking discussions, inspiring stories, entertaining performances, and educational documentaries. Through the Common Screen channel, viewers are invited to explore the commonalities that bind us together as human beings, while also appreciating and respecting our differences. The channel aims to foster understanding, empathy, and unity by providing a space where people can engage with diverse perspectives, engage in meaningful conversations, and broaden their horizons. From showcasing cultural celebrations to highlighting social issues, the Common Screen channel strives to create a positive impact on society. It seeks to bridge gaps, break down barriers, and promote a sense of belonging for everyone who tunes in. By promoting open-mindedness, dialogue, and the exchange of ideas, Common Screen aspires to be a catalyst for positive change and a source of inspiration for viewers around the world.