"expressionlet" meaning in All languages combined

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "expression",
        "3": "let"
      },
      "expansion": "expression + -let",
      "name": "suffix"
    }
  ],
  "etymology_text": "From expression + -let.",
  "forms": [
    {
      "form": "expressionlets",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "expressionlet (plural expressionlets)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
          ],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "English terms suffixed with -let",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with 1 entry",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with entries",
          "parents": [],
          "source": "w"
        }
      ],
      "examples": [
        {
          "ref": "2015, Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen, “Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition”, in arXiv:",
          "text": "Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) formed by dense low-level features; 2) a Universal Manifold Model (UMM) is learned over all low-level features and represented as a set of local modes to statistically unify all the STMs. 3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local mode.",
          "type": "quote"
        }
      ],
      "glosses": [
        "A small part of an expression"
      ],
      "id": "en-expressionlet-en-noun-XaEhugl0",
      "links": [
        [
          "expression",
          "expression"
        ]
      ]
    }
  ],
  "word": "expressionlet"
}

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "expression",
        "3": "let"
      },
      "expansion": "expression + -let",
      "name": "suffix"
    }
  ],
  "etymology_text": "From expression + -let.",
  "forms": [
    {
      "form": "expressionlets",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "expressionlet (plural expressionlets)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        "English countable nouns",
        "English entries with incorrect language header",
        "English lemmas",
        "English nouns",
        "English terms suffixed with -let",
        "English terms with quotations",
        "Pages with 1 entry",
        "Pages with entries"
      ],
      "examples": [
        {
          "ref": "2015, Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen, “Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition”, in arXiv:",
          "text": "Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) formed by dense low-level features; 2) a Universal Manifold Model (UMM) is learned over all low-level features and represented as a set of local modes to statistically unify all the STMs. 3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local mode.",
          "type": "quote"
        }
      ],
      "glosses": [
        "A small part of an expression"
      ],
      "links": [
        [
          "expression",
          "expression"
        ]
      ]
    }
  ],
  "word": "expressionlet"
}