Row 26855
Content Data
This page contains data entry 26855 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
The simplest way is to use the same space, like in CLIP, but necessarily. There are many transformer papers with token-level fusion where you just mix unaligned tokens from two modalities with a few more transformer layer, e.g. ViLT.
They even explicitly add modality-specific vectors to both kinds of tokens to further help the model differentiate between them, so your intuition is somewhat good.
| Field | Value |
|---|---|
| text | The simplest way is to use the same space, like in CLIP, but necessarily. There are many transformer papers with token-level fusion where you just mix unaligned tokens from two modalities with a few more transformer layer, e.g. ViLT. They even explicitly add modality-specific vectors to both kinds of tokens to further help the model differentiate between them, so your intuition is somewhat good. |
| label | r/machinelearning |
| dataType | comment |
| communityName | r/MachineLearning |
| datetime | 2024-05-21 |
| username_encoded | Z0FBQUFBQm5Lak1EVXc1UGYtYlpNM2NaSTB4U0QwbUc4bEVDalBvTWpYMldGaUdQV2dyb2xmWHo2aVd3cl9pb3hBWGFXcEQ5ZlhSVUZkcDB6VFoyZDFaUTY3NnY5dXlzeXc9PQ== |
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Raw Record
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"text": "The simplest way is to use the same space, like in CLIP, but necessarily. There are many transformer papers with token-level fusion where you just mix unaligned tokens from two modalities with a few more transformer layer, e.g. ViLT.\n\nThey even explicitly add modality-specific vectors to both kinds of tokens to further help the model differentiate between them, so your intuition is somewhat good.",
"label": "r/machinelearning",
"dataType": "comment",
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"datetime": "2024-05-21",
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Entry Information
- Entry ID: 26855
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000