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1.6 million annotations. 238,906 video clips, 624,430 keyframes. Annotations, videos. Action prediction 2020 [122] [123] Li et al from Perception Team of Google AI. TV Human Interaction Dataset Videos from 20 different TV shows for prediction social actions: handshake, high five, hug, kiss and none. None. 6,766 video clips video clips Action ...
Dollhouse for a dollhouse scale for 1:12 dollhouses. Commonly used for mini armor. Used for 12 mm, and 12.5 mm figure scale miniature wargaming. 1:128: 3 ⁄ 32 in: 2.381 mm A few rockets and some fit-in-the-box aircraft are made to this size. 1:120: 0.1 in: 2.54 mm: Model railways (TT) Derived from the scale of 1 inch equals 10 feet.TT model ...
ITU-T Rec. P.1204.3: Video This model uses the video bitstream to analyze compression/coding quality based on features like quantization parameters and motion vectors. [14] ITU-T Rec. P.1204.5: Video This is a hybrid model that uses the decoded pixels and information about the video codec to determine final video quality. [14]
Academy Awards; Adobe Premiere—real time editing; Advanced Authoring Format AAF; alpha channel; Animation; Audio commentary; Avid—real time editing; B-movie; B-roll; Betacam; Betamax; Blu-ray
A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. [1] Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models .
9-slice scaling (also known as Scale 9 grid, 9-slicing or 9-patch) is a 2D image resizing technique to proportionally scale an image by splitting it in a grid of nine parts. [ 1 ] The key idea is to prevent image scaling distortion by protecting the pixels defined in 4 parts (corners) of the image and scaling or repeating the pixels in the ...
Number Nine Visual Technology Corporation was a manufacturer of video graphics chips and cards from 1982 to 1999. Number Nine developed the first 128-bit graphics processor (the Imagine 128), as well as the first 256-color (8-bit) and 16.8 million color (24-bit) cards.
In the original OpenAI CLIP report, they reported training 5 ResNet and 3 ViT (ViT-B/32, ViT-B/16, ViT-L/14). Each was trained for 32 epochs. The largest ResNet model took 18 days to train on 592 V100 GPUs. The largest ViT model took 12 days on 256 V100 GPUs. All ViT models were trained on 224x224 image resolution.