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	अटेंशन मैकेनिज्म (Attention Mechanism)	भाव विश्लेषण	विजन ट्रांसफार्मर
क्षेत्र	गहन अधिगम	पाठ खनन	गहन अधिगम
परिवार	Machine learning	Process / pipeline	Machine learning
उद्भव वर्ष	2015	—	2021
प्रवर्तक	Bahdanau, D.; Luong, M.T.	—	Dosovitskiy, A. et al.
प्रकार	Neural attention layer (encoder-decoder)	NLP text-classification task	Transformer architecture for images (self-attention over patches)
मौलिक स्रोत	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
उपनाम	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	opinion mining, polarity detection, duygu analizi	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
संबंधित	5	3	5
सारांश	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).