Results for “visual-grouping”

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

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

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

SNLI-VE: Visual Entailment Dataset

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).

Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.

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

Ask visual questions about video clips using a VSS agent's video_understanding tool, requiring a fresh look at frames rather than prior metadata or search results.

Trains, evaluates, exports, and runs inference for Visual ChangeNet models used in AOI defect detection, comparing image pairs for PASS/NO_PASS classification or change-segmentation masks.

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.

Evaluates text-to-image models on spatial relationship accuracy using the VISOR metric, separating object detection from spatial correctness to reveal biases like object priority and merging.

Flamingo: A Visual Language Model for Few-Shot Learning

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

Organize qualitative research data into an affinity diagram with themes, clusters, and insight statements.

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.

Ingests video and audio from files, URLs, RTSP feeds, or desktop capture; indexes and searches moments with timestamps; transcodes, edits timelines, generates media assets, and creates real-time alerts for live streams.

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.

Visual design and asset creation — social media graphics, HTML/CSS mockups, image generation, text overlays and branding for any active brand

Grit: General Robust Image Task Benchmark

Agent profile for inspect screenshots, viewports, layout overlaps, visual regressions, spacing, typography, and interaction states. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

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.

Manage Kafka consumer groups on Redpanda with rpk group: list, describe, seek, delete groups, and delete offsets.

Establish clear visual hierarchy through size, weight, color, spacing, and positioning to guide users through interfaces.

Towards Open-World Segmentation of Parts

SVIT: Scaling up Visual Instruction Tuning