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Machine learningDeep learning / NLP / CV

Multimodal Long Short-Term Memory Network

LSTM ya kawaida husoma mkondo mmoja wa tokeni na hukumbuka kinacho maana katika hatua za muda. Multimodal LSTM inauliza: vipi ikiwa pembejeo sio maneno tu, bali pia sauti ya sauti, maonyesho ya uso, au fremu za picha — zote zikifunuka kwa wakati? Uelewa mkuu ni kwamba kila njia hubeba ishara zinazokamilishana, na kuziuunganisha — ama kwa kuunganisha vekta zao za sifa katika kila hatua, kujifunza hali ya seli ya pamoja, au kutumia malango maalum — huruhusu mtandao kuchukua fursa ya uhusiano kati ya njia ambazo hakuna mkondo unaofunua peke yake. Matokeo yake ni mtindo mfuatano tajiri zaidi unaoona picha kamili.

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Vyanzo

  1. Rajagopalan, S., Tran, L., Rozgic, V., Narayanan, S., Kumar, A., & Ramakrishna, S. (2016). Extending Long Short-Term Memory for Multi-View Structured Learning. In Proceedings of ECCV 2016. Springer. link
  2. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Multimodal Long Short-Term Memory Network. ScholarGate. https://scholargate.app/sw/deep-learning/multimodal-lstm

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ScholarGateMultimodal LSTM (Multimodal Long Short-Term Memory Network). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/multimodal-lstm · Seti ya data: https://doi.org/10.5281/zenodo.20539026