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video    音标拼音: [v'ɪdio]
n. U电视,视频;录像
a. 电视的,视频的;录像的

U电视,视频;录像电视的,视频的;录像的

video
视 视频

video
n 1: the visible part of a television transmission; "they could
still receive the sound but the picture was gone" [synonym:
{video}, {picture}]
2: a recording of both the visual and audible components
(especially one containing a recording of a movie or
television program) [synonym: {video recording}, {video}]
3: (computer science) the appearance of text and graphics on a
video display
4: broadcasting visual images of stationary or moving objects;
"she is a star of screen and video"; "Television is a medium
because it is neither rare nor well done" - Ernie Kovacs
[synonym: {television}, {telecasting}, {TV}, {video}]

Moving images presented as a sequence of {static
images} (called "frames") representing snapshots of the scene,
taken at regularly spaced time intervals, e.g. 50 frames per
second. Apart from the frame rate, other important properties
of a video are the {resolution} and {colour depth} of the
individual images.

Digital video data is typically stored and transmitted in a
format like {MPEG} or {H.264} that includes synchoronised
{sound}.

Unlike broadcast {television}, digital video on a computer or
network uses {compression}. Compression is even more
important for video that for static images due to the large
amount of data involved in even a short video. Furthermore,
compression allows video to be transmitted via a channel whose
bandwidth is less than the raw data rate implied by the
resolution and frame rate. This allows the recipient to start
displaying the video before the transmission is complete, a
process known as {streaming}.

Compression can be relatively slow but decompression is done
in {real-time} with the picture quality and {frame rate}
varying with the processing power available and the size and
scaling of the picture.

There are many types of software for displaying video on
computers including {Windows Media Player} from {Microsoft},
{QuickTime} from {Apple Computer}, {DivX}, {VLC}, {RealPlayer}
and {Acorn Computers}' {Replay}.

(2011-01-04)

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  • DepthAnything Video-Depth-Anything - GitHub
    This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth accuracy
  • 【EMNLP 2024 】Video-LLaVA: Learning United Visual . . . - GitHub
    Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update 💡 I also have other video-language projects that may interest you Open-Sora Plan: Open-Source Large Video Generation Model
  • GitHub - MME-Benchmarks Video-MME: [CVPR 2025] Video-MME: The First . . .
    We introduce Video-MME, the first-ever full-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video analysis It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities
  • Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video . . .
    Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding This is the repo for the Video-LLaMA project, which is working on empowering large language models with video and audio understanding capabilities
  • Generate Video Overviews in NotebookLM - Google Help
    Video Overviews, including voices and visuals, are AI-generated and may contain inaccuracies or audio glitches NotebookLM may take a while to generate the Video Overview, feel free to come back to your notebook later
  • GitHub - k4yt3x video2x: A machine learning-based video super . . .
    A machine learning-based video super resolution and frame interpolation framework Est Hack the Valley II, 2018 - k4yt3x video2x
  • Wan: Open and Advanced Large-Scale Video Generative Models
    Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan2 1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation Wan2 1 offers these key features:
  • Troubleshoot YouTube video errors - Google Help
    Check the YouTube video’s resolution and the recommended speed needed to play the video The table below shows the approximate speeds recommended to play each video resolution
  • Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub
    Video-R1 significantly outperforms previous models across most benchmarks Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35 8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the
  • VideoLLM-online: Online Video Large Language Model for Streaming Video
    Online Video Streaming: Unlike previous models that serve as offline mode (querying responding to a full video), our model supports online interaction within a video stream It can proactively update responses during a stream, such as recording activity changes or helping with the next steps in real time





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