  Astounding results from Transformer models on natural language tasks have
intrigued the vision community to study their application to computer vision
problems. Among their salient benefits, Transformers enable modeling long
dependencies between input sequence elements and support parallel processing of
sequence as compared to recurrent networks e.g., Long short-term memory (LSTM).
Different from convolutional networks, Transformers require minimal inductive
biases for their design and are naturally suited as set-functions. Furthermore,
the straightforward design of Transformers allows processing multiple
modalities (e.g., images, videos, text and speech) using similar processing
blocks and demonstrates excellent scalability to very large capacity networks
and huge datasets. These strengths have led to exciting progress on a number of
vision tasks using Transformer networks. This survey aims to provide a
comprehensive overview of the Transformer models in the computer vision
discipline. We start with an introduction to fundamental concepts behind the
success of Transformers i.e., self-attention, large-scale pre-training, and
bidirectional encoding. We then cover extensive applications of transformers in
vision including popular recognition tasks (e.g., image classification, object
detection, action recognition, and segmentation), generative modeling,
multi-modal tasks (e.g., visual-question answering, visual reasoning, and
visual grounding), video processing (e.g., activity recognition, video
forecasting), low-level vision (e.g., image super-resolution, image
enhancement, and colorization) and 3D analysis (e.g., point cloud
classification and segmentation). We compare the respective advantages and
limitations of popular techniques both in terms of architectural design and
their experimental value. Finally, we provide an analysis on open research
directions and possible future works.
