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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.

Little Stars Music - Sing Along Nursery Rhymes & Kids Songs

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Welcome to Little Stars Music! 🌟 Discover a world of enchanting nursery rhymes and delightful kids' songs designed to make learning fun and memorable. Little Stars Music is your go-to channel for sing-along favorites, playful melodies, and educational tunes that captivate young minds. What We Offer: 🎵 Classic Nursery Rhymes: From "Twinkle Twinkle Little Star" to "Old MacDonald," we bring timeless rhymes to life with vibrant animations. 🎤 Fun Sing-Alongs: Encourage your little ones to sing along with catchy songs that boost language skills and rhythm awareness. 📚 Educational Songs: Learning through music is easy with our collection of alphabet songs, counting tunes, and more. 🎨 Colorful Animations: Our videos are filled with bright, engaging visuals that keep children entertained while they learn. Perfect For: Babies, toddlers, and preschoolers Parents looking for educational and entertaining content Teachers and caregivers seeking fun classroom resources Why Choose Little Stars Music? At Little Stars Music, we believe that every child is a star. Our channel is dedicated to nurturing creativity, imagination, and a love for music from an early age. Whether it's playtime or bedtime, our songs are perfect for every occasion. Join Our Community: Subscribe to Little Stars Music and let your child’s musical journey begin! Don't forget to hit the notification bell 🔔 to stay updated on our latest videos. #LittleStarsMusic #NurseryRhymes #KidsSongs #SingAlong #ChildrensMusic #ToddlerTunes #EducationalSongs #BabyMusic #PreschoolLearning