Results for “vision-transformer”

Run state-of-the-art machine learning models directly in JavaScript/TypeScript across browsers and server-side runtimes using Transformers.js.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

LongVA: Long Context Transfer from Language to Vision

Masked Autoencoders Are Scalable Vision Learners

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

Enables visual instruction tuning and image-based conversations using open-source vision-language models. Supports multi-turn image chat, visual question answering, and image understanding tasks.

Optimizes hot-path scalar loops in .NET 8+ with cross-platform Vector128/Vector256/Vector512 SIMD intrinsics, or replaces manual math loops with single TensorPrimitives API calls.

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

Runs the open-source LLaVA vision-language model for image understanding, captioning, visual question answering, and multi-turn image conversations, including setup, inference, and training guidance.

Scaling Vision with Sparse Mixture of Experts

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

Inspect, generate, transform, and visualize UV channels on StaticMesh assets (UVMappingService). Use when the user asks to check/add/remove UV channels, auto-unwrap UVs, transform (tile/offset/rotate) UVs, check UV health/islands, or export a UV layout image.

Builds Vue 3 components with Composition API patterns, configures Nuxt 3 SSR/SSG projects, sets up Pinia stores, scaffolds Quasar/Capacitor mobile apps, implements PWA features, and optimises Vite builds.

Turn a design or spec document into a working MVP by planning thin vertical slices, scaffolding the first slice, then building iteratively with test-driven development and gated commits.

PaLI-3: Smaller, Faster, Stronger

Assists with visual effects compositing workflows including green screen, particle effects, and color matching

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Control the Unreal Editor level viewport — camera type/position, view mode, FOV, exposure, layout, and rendering settings (ViewportService). Use when the user asks to move the editor camera, change the view mode (Lit/Unlit/Wireframe), set FOV/exposure, switch viewport layout, or frame the level for a screenshot.

Vision-language chat: VQA, captioning, image dialogue.

Multimodal Learning with Transformers: A Survey