Row 49096

Row ID: 49096 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 49096 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

The choice is up to you when you design your network.

I'm aware of two ways of handling it:

* 3D Volume: A video can be represented as a 3D volume where:

- Width and height correspond to the spatial dimensions of a single frame. - Depth represents the number of frames (or channels depending on the data format).

* Convolution Operation:

- The conv3d layer applies a filter (kernel) across all three dimensions. This essentially captures spatial features (like edges or corners) across neighboring pixels within a frame and also extracts features across consecutive frames.

FieldValue
text The choice is up to you when you design your network. I'm aware of two ways of handling it: * 3D Volume: A video can be represented as a 3D volume where: - Width and height correspond to the spatial dimensions of a single frame. - Depth represents the number of frames (or channels depending on the data format). * Convolution Operation: - The conv3d layer applies a filter (kernel) across all three dimensions. This essentially captures spatial features (like edges or corners) across neig…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-22
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Raw Record

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  "text": "The choice is up to you when you design your network.\n\nI'm aware of two ways of handling it:\n\n* 3D Volume: A video can be represented as a 3D volume where:\n\n  - Width and height correspond to the spatial dimensions of a single frame.\n  - Depth represents the number of frames (or channels depending on the data format).\n\n* Convolution Operation:\n\n - The conv3d layer applies a filter (kernel) across all three dimensions. This essentially captures spatial features (like edges or corners) across neighboring pixels within a frame and also extracts features across consecutive frames.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1SajhKN2ZPWFJWSTRIQ3c2NTNJM3p5YzdCTHNzTWJNc0JoMVhSTjR6eW1QSk1mYWNzSTV2QTNLcjFpQS1mbFpBWGRVNlcxQWVRUE1BY0lybF83OHJJeDNXTGszd2xLaTZTZjk4V2xNM3kzVUU9",
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}
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