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A Blend of Everything

10 Followers

Welcome to Coctail Hub! Dive into a vibrant mix of content where anything and everything is on the table. From inspiring gymnastics routines and insightful health tips to daily life adventures, self-improvement journeys, and motivational talks, Coctail Hub is your one-stop destination for entertainment, growth, and inspiration. Join us as we blend creativity, funny , fitness, and life’s randomness into a unique cocktail of videos. Whether you're looking to learn, laugh, or level up, Coctail Hub has something for everyone. Hit subscribe and be part of our dynamic community where every day is a new mix!

Blender Dumbed Down

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Welcome to LM3OFFICIAL – Dumbed-Down Blender 3D Tutorials for Everyone! Ready to dive into the world of 3D design without such a steep learning curve? You’ve come to the right place! On this channel, we break down Blender’s powerful features into easy-to-follow, beginner-friendly tutorials that help you master 3D modeling, animation, and rendering, step by step. Whether you’re a complete newbie or looking to sharpen your skills, you’ll find clear, concise lessons designed to make 3D creation accessible and fun. From basic shapes to more advanced projects, each video guides you through the process in a way that’s simple to understand and exciting to explore.

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.

2 Followers

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.