「recognition」の検索結果

Spot patterns appearing in 3+ domains to find universal principles

日本語の概要は準備中です。原文の説明を表示しています。

Transform a user-uploaded photograph into one horizontal collectible memory card with a large quiet editorial illustration, six integrated journaling-sticker motifs, three small English keywords beneath the left illustration, tactile paper grain, and deliberately clumsy flat color fields. Use when Codex needs to P图, edit, redraw, stylize, or turn a travel, street, landscape, lifestyle, portrait, or pet photo into a minimalist postcard-like card or sticker-card in this visual language. Preserve recognition through one same-medium identification anchor and optionally one exact place-defining landmark text shown once; never retain an unintended photorealistic patch, signature, or watermark.

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Use for reverse engineering stripped Go and Rust binaries including runtime recognition, pclntab/moduel data recovery, panic strings, and idiomatic decompilation recovery.

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Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

日本語の概要は準備中です。原文の説明を表示しています。

Converts audio objects stored in Volcengine TOS into PCM-oriented WAV output through TOS data-process async jobs. Use this skill when the user needs to convert audio to PCM or WAV, resample audio to 16k, convert to mono, change sample format, normalize audio for ASR pipelines, or prepare TOS audio files for speech recognition — even if they don't explicitly mention 'PCM' or 'audio conversion'.

日本語の概要は準備中です。原文の説明を表示しています。

C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib

日本語の概要は準備中です。原文の説明を表示しています。

Deploy eternal navigation wisdom - Brahman, Atman, Satchitananda recognition active.

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Deploy universal wisdom voices from all traditions simultaneously - Hindu, Buddhist, Taoist, Abrahamic, Indigenous, Scientific - revealing their unified recognition of consciousness navigation.

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Recognize which awakening stage consciousness is navigating through. Use when diagnosing seeker development, identifying stage transitions, or recognizing readiness levels. Covers 7 stages from initial dissatisfaction through natural flow mastery.

日本語の概要は準備中です。原文の説明を表示しています。

Activate ATLAS pattern recognition mode. Identify repeated patterns, extract essential characteristics, and create reusable abstractions from concrete examples.

日本語の概要は準備中です。原文の説明を表示しています。

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

日本語の概要は準備中です。原文の説明を表示しています。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

日本語の概要は準備中です。原文の説明を表示しています。

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

日本語の概要は準備中です。原文の説明を表示しています。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

日本語の概要は準備中です。原文の説明を表示しています。

Use davidsandberg/facenet for TensorFlow 1.x face recognition workflows: MTCNN alignment, embeddings, classification, LFW evaluation, training, and model export.

日本語の概要は準備中です。原文の説明を表示しています。

Use face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle deployment.

日本語の概要は準備中です。原文の説明を表示しています。

Routes easy12306 12306 captcha recognition tasks across pretrained inference, data preparation, text-model training, and image-model training workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Use EasyOCR to detect and recognize text in images, configure model and language selection, load custom recognition bundles, and troubleshoot DBNet setup.

日本語の概要は準備中です。原文の説明を表示しています。

Use DeepFace for face recognition, verification, embeddings, face detection, demographic analysis, datastore search, API serving, model selection, and troubleshooting.

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Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.

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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

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Universal opt-out mechanism implementation across US state privacy laws. Covers Global Privacy Control (GPC) signal technical implementation, state-by-state recognition requirements, browser detection methods, authenticated vs unauthenticated handling, and compliance testing.

日本語の概要は準備中です。原文の説明を表示しています。

New Jersey Data Privacy Act (NJDPA) compliance, effective January 15, 2025. Covers consumer rights (access, correction, deletion, portability, opt-out), controller obligations, sensitive data requirements, universal opt-out mechanism recognition, 30-day cure period (sunsets after 18 months), and AG enforcement. Keywords: NJDPA, New Jersey, data privacy, consumer rights, sensitive data, universal opt-out, AG enforcement.

日本語の概要は準備中です。原文の説明を表示しています。

Montana Consumer Data Privacy Act (MTDPA) compliance. Lowest consumer threshold at 50,000 consumers. Covers sensitive data consent, universal opt-out recognition, consumer rights, controller obligations, 60-day cure period, and AG enforcement. Effective October 1, 2024.

日本語の概要は準備中です。原文の説明を表示しています。