Transcription of 1 Transformers in Vision: A Survey
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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.
introduction to the salient concepts underlying Transformer networks and then elaborate on the specifics of recent vision transformers. Where ever possible, we draw parallels be-tween the Transformers used in the NLP domain [1] and the ones developed for vision problems to flash major novelties and interesting domain-specific insights.
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