49 件 ・ 関連度順
概要と使いどころ
Use when adding multimedia to PyQt/PySide6 apps - audio and video playback with QMediaPlayer, camera capture with QCamera and QMediaCaptureSession, audio/video recording with QMediaRecorder, GStreamer backend setup, or codec and platform compatibility issues
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 when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.
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 when building or auditing the related-work section of an ACM MM (ACM Multimedia) paper — covering the multimedia literature spread across vision, audio/speech, language, HCI/QoE, and systems, handling arXiv-speed concurrency, keeping citations double-blind, and verifying that cited "ACM MM papers" are ACM MM and not ICMR, MMSys, CVPR, or TOMM.
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。
Interacting with Mediabunny
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。
Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
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。
电商运营与内容创作者在需要制作带货短视频时，用此技能一键生成9:16竖屏数字人成片。自动编排AI绘画、语音合成与视频生成，快速产出“小省导购员”专属形象与专业配音，让多模态视频制作省时省力。
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。
Interacting with Mediabunny
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。
Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.
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。
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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 when preparing the ACM MM (ACM Multimedia) camera-ready version of record — de-anonymizing safely, completing the ACM rights form and CCS concepts, meeting ACM sigconf requirements, releasing code/data/media artifacts and any earned reproducibility badge, registering, and planning the oral/poster presentation in Rio.
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 when planning the end-to-end ACM MM (ACM Multimedia) campaign calendar — from thematic-area scoping and track choice, through the April OpenReview abstract/paper/supplement chain, the June anonymous rebuttal, the July decision, the August camera-ready, and the November presentation in Rio, with the right skill invoked at each stage.
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 when packaging code, models, datasets, or media as ACM MM (ACM Multimedia) artifacts — building the anonymous review package versus the public release, and choosing between the Open Source Software Competition, the Dataset track, the Reproducibility track, and main-track supplementary evidence, each with its own blinding and expectations.
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 when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.
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 when auditing an ACM MM (ACM Multimedia) submission for OpenReview readiness — thematic-area choice, the 6-8 page ACM sigconf budget, references-only overflow, double-blind anonymity versus the single-blind Reproducibility/Open-Source/Dataset tracks, supplementary media, dual submission, desk-reject triggers, and last-week sequencing.
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 when reasoning about the ACM MM (ACM Multimedia) review pipeline — thematic-area routing to reviewers and area chairs, the OpenReview double-blind process and its single-blind track exceptions, the optional anonymous rebuttal, the meta-review and decision, and the oral/poster and award tiers, and where an author actually has leverage.
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 when deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.
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 when organizing the ACM MM (ACM Multimedia) supplementary material due after the paper deadline — deciding what belongs in the 6-8 page body versus the supplement, packaging video/audio/interactive demos that render on a reviewer's machine, keeping all assets anonymous for double-blind tracks, and pointing to code and data.
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 when revising an ACM MM (ACM Multimedia) paper for house style — putting the cross-modal contribution on the first page, framing media (figures, video, audio) as evidence rather than decoration, making the fusion the visible claim, and compressing the argument into a 6-8 page ACM sigconf body with references-only overflow.
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 when drafting the ACM MM (ACM Multimedia) rebuttal — turning multiple anonymous reviews into one focused, anonymous response that fixes factual errors first, adds small confirmatory cross-modal results, concedes calibratedly, and stays inside policy (anonymous, no new external links), written for the area chair who decides.
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 when organizing AAAI supplementary material, including the technical appendix, multimedia appendix, and code/data ZIPs, while respecting that AAAI supplements are due with the paper, treated as immutable after submission, must stay double-blind, and should never hide main-paper-critical evidence from reviewers who skim the appendix.
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 when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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。
Building or editing Simulink (.slx) models that stream signals in frames using DSP System Toolbox — audio, vibration, radar, comms. Use when the prompt names a Simulink block (Discrete FIR Filter, Buffer, Unbuffer, Rate Transition, Downsample, Upsample, Sample-Rate Converter, FIR Decimation, Time Scope, Spectrum Analyzer, From Multimedia File) or an action (frame-based processing, InputProcessing, buffering, overlap, windowing, STFT, short-time Fourier, decimate, downsample, upsample, resample, multirate, anti-aliasing, tunable filter). Prevents silent numerical errors from sample-vs-frame mismatch, wrong block choice, or missing anti-aliasing.
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。
When the user wants to optimize content for SEO—word count, H2 keywords, keyword density, multimedia, tables, lists. Also use when the user mentions "content length," "word count," "keyword stuffing," "H2 keywords," "keyword density," "tables," "bullet points," or "content structure." For keywords, use keyword-research.
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。
Generate text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M3 model), image generation (image-01), video generation (Hailuo-2.3), speech synthesis (speech-2.8-hd, 300+ voices), music generation (music-2.6 with lyrics, cover, and instrumental), and web search. Use when the user needs to create AI-generated multimedia content, produce narrated audio from text, compose music, or search the web through MiniMax AI services.
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。