TRANS-VQA : Fully Transformer-Based Image Question-Answering Model Using Question-guided Vision Attention
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20240013271 | ||
| 003 | MAP | ||
| 005 | 20240829104256.0 | ||
| 008 | 240829e20240619esp|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a922.134 | ||
| 245 | 1 | 0 | $aTRANS-VQA$b: Fully Transformer-Based Image Question-Answering Model Using Question-guided Vision Attention$cDipali Koshti [et al.] |
| 520 | $aUnderstanding multiple modalities and relating them is an easy task for humans. But for machines, this is a stimulating task. One such multi-modal reasoning task is Visual question answering which demands the machine to produce an answer for the natural language query asked based on the given image. Although plenty of work is done in this field, there is still a challenge of improving the answer prediction ability of the model and breaching human accuracy. A novel model for answering image-based questions based on a transformer has been proposed. The proposed model is a fully Transformer-based architecture that utilizes the power of a transformer for extracting language features as well as for performing joint understanding of question and image features. The proposed VQA model utilizes F-RCNN for image feature extraction | ||
| 650 | 4 | $0MAPA20080611200$aInteligencia artificial | |
| 650 | 4 | $0MAPA20080541408$aImagen | |
| 650 | 4 | $0MAPA20080548056$aMáquinas | |
| 650 | 4 | $0MAPA20080617479$aLenguajes de programación | |
| 700 | 1 | $0MAPA20240020842$aKoshti, Dipali | |
| 773 | 0 | $wMAP20200034445$g19/06/2024 Volumen 27 Número 73 - junio 2024 , p.11-128$x1988-3064$tRevista Iberoamericana de Inteligencia Artificial$d : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- | |
| 856 | $uhttps://journal.iberamia.org/index.php/intartif/article/view/1252 |