Results for “captioning”

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.

Provides a Caption type definition and links to skills for transcribing, displaying, and importing captions in Remotion videos.

Open-Vocabulary Object Detection Using Captions

CapsFusion: Rethinking Image-Text Data at Scale

DreamLIP: Language-Image Pre-training with Long Captions

LaCLIP: Improving CLIP Training with Language Rewrites

COCO: Microsoft COCO: Common Objects in Context

Create subtitles or captions with correct timing, line breaks, and reading-speed limits.

DALL-E 3: Improving Image Generation with Better Captions

Packages an existing talking-head, interview, or podcast video with timed, designed graphic overlay cards—kinetic titles, lower-thirds, data callouts, quotes, side panels, picture-in-picture—synced to the transcript, on a 16:9, 9:16, or 4:5 canvas.

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.

Visual Instruction Tuning

Re-render a hero image into aspect ratios for Instagram, TikTok, YouTube Shorts, and Twitter/X.

Generates a single-frame HTML template with a cinematic film-leak aesthetic: warm light leaks, 35mm grain, 2.39:1 letterbox, and serif typography for opening titles or chapter cards.

Generate high-CTR YouTube thumbnails with striking imagery, bold text placement, and emotional subjects using AI image generation.

吴京（演员 / 导演）认知与表达框架（压缩蒸馏）：主旋律英雄壳、直男效能叙事 触发：战狼、流浪地球 等。非煽动仇恨

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

Applies dialogue MA, portrait reframing, and loudness/sync QA to an interview or talking-head rough cut using canonical timeline metadata and a shared renderer.

Police the project's vocabulary — bust vague terms, keep the CONTEXT.md glossary sharp, and lock in decisions worth remembering as ADRs. Use when the user debates naming, says "what should we call this", asks to pin down terminology, wants a decision recorded, or when another skill (hot-seat, whiteboard) surfaces a decision that clears the ADR bar. Just reading the glossary for vocabulary is NOT this skill — trigger only when the words or decisions are being changed.

Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.

Generate artistic top-down photos of keyboard keycaps arranged to spell out custom text messages.

构建可复用的Manim解释器，用于技术概念、图表、系统图和产品演示，并在需要时移交给更广泛的ECC视频栈。当用户希望获得清晰的动画解释而非通用的人物讲解脚本时使用。

Build a visual sequence that proves, explains, and supports decisions.

当需要在写作中与读者建立情感连接、放大问题严重性，并引出后续解决方案时

GLIP: Grounded Language-Image Pre-training

当需要从现有内容中创造新意，或避免内容同质化时

上下文压缩省 Token。对话变长或开新任务时输出/使用 6 块压缩结构，总长 400~800 tokens。必加载。

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.

Generates structured VGL JSON prompts for Bria's FIBO image generation models, covering text-to-image, editing, inpainting, outpainting, and captioning with a deterministic schema.