Results for “visual-grouping”

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

Group interface elements using containers, backgrounds, and boundaries based on the Gestalt Law of Common Region.

COCO: Microsoft COCO: Common Objects in Context

NLVR2: A Visual Reasoning Benchmark for Natural Language

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.

Skeptical pushback on visual placement — both density and quality. Reads the annotated outline plus the visuals manifest and asks (a) whether the article hits the density target from editorial-principles-visuals.md, (b) whether each [VISUAL:...] earns its place, (c) whether sections without one would benefit. One revision pass on FAIL (BLOG_AGENT_VISUALS_REVISION_BUDGET, default 1).

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.

VILA: On Pre-training for Visual Language Models

This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.

Realize every typed [VISUAL:...] placeholder in the cited draft into an actual asset — Playwright screenshots of brand UI, Replicate-generated images (GPT Image 2 default, Nano Banana backup), matplotlib charts. Manual-capture flag for video/external/gif/adult-image. Updates the draft to reference local image paths.

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

Create a globe-like 3D particle visualization with a dense luminous spherical core and thinner orbital ring or flattened disc. Use when a design needs a premium planetary, orbital, synthesized data-globe effect rendered with real WebGL/Three.js particles, not generic starfields or full page layout changes.

Video and audio perception, indexing, and editing. Ingest files/URLs/live streams, build visual/spoken indexes, search with timestamps, edit timelines, add overlays/subtitles, generate media, and create real-time alerts.

Watch and understand video files by converting them to viewable image storyboards for enjoyment, analysis, species identification, behavior tracking, and comprehension

Visual Instruction Tuning

Video and audio perception, indexing, and editing. Ingest files/URLs/live streams, build visual/spoken indexes, search with timestamps, edit timelines, add overlays/subtitles, generate media, and create real-time alerts.

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.

DriveLM: Driving with Graph Visual Question Answering

Find adjacent vendors and subs (not top market primes) who fill a capability gap against a displacement target using USASpending flows and SAM entity signals. Use when user defines a teaming gap and wants vault-ready partner shortlist with citations.

Towards Open-World Segmentation of Parts

SVIT: Scaling up Visual Instruction Tuning

Design clear, accessible data visualizations with appropriate chart selection and styling.

This skill provides guidance for video analysis and processing tasks using computer vision techniques. It should be used when analyzing video frames, detecting motion or events, tracking objects, extracting temporal data (e.g., identifying specific frames like takeoff/landing moments), or performing frame-by-frame processing with OpenCV or similar libraries.

Ingest, index, search, and edit video and live streams with timestamps, subtitles, overlays, and real-time alerts.

Creates data-driven infographics and charts as accessible SVG. Use when visualizing data, choosing a chart type, generating an SVG chart or infographic, or reviewing a visualization for clarity and accuracy.

GLIP: Grounded Language-Image Pre-training

Extract brand visual system from a website or brand assets into a structured style guide

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.

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