Transcription of 1 Transformers in Vision: A Survey
{{id}} {{{paragraph}}}
Example: air traffic controller
1 Transformers in Vision: A Survey
1 Transformers in Vision: A SurveySalman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir,Fahad Shahbaz Khan, and Mubarak ShahAbstract Astounding results from Transformer models on natural language tasks have intrigued the vision community to study theirapplication to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between inputsequence elements and support parallel processing of sequence as compared to recurrent , Long short-term memory(LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suitedas set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities ( , images, videos,text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and hugedatasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks.
which is then normalized using softmax operator to get the attention scores. Each entity then becomes the weighted sum of all entities in the sequence, where weights are given by the attention scores (Fig.2and Fig.3, top row-left block). Masked Self-Attention: The standard self-attention layer attends to all entities. For the Transformer model [1]
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: