%20and%20are%20increasingly%20applied%20to%20computer%20vision%20tasks%20with%20remarkable%20success.%20These%20models%2C%20renowned%20for%20their%20ability%20to%20capture%20long-range%20dependencies%20and%20contextual%20information%2C%20offer%20a%20promising%20alternative%20to%20traditional%20convolutional%20neural%20networks%20(CNNs)%20in%20computer%20vision.%20In%20this%20review%20paper%2C%20we%20provide%20an%20extensive%20overview%20of%20various%20transformer%20architectures%20adapted%20for%20computer%20vision%20tasks.%20We%20delve%20into%20how%20these%20models%20capture%20global%20context%20and%20spatial%20relationships%20in%20images%2C%20empowering%20them%20to%20excel%20in%20tasks%20such%20as%20image%20classification%2C%20object%20detection%2C%20and%20segmentation.%20Analyzing%20the%20key%20components%2C%20training%20methodologies%2C%20and%20performance%20metrics%20of%20transformer-based%20models%2C%20we%20highlight%20their%20strengths%2C%20limitations%2C%20and%20recent%20advancements.%20Additionally%2C%20we%20discuss%20potential%20research%20directions%20and%20applications%20of%20transformer-based%20models%20in%20computer%20vision%2C%20offering%20insights%20into%20their%20implications%20for%20future%20advancements%20in%20the%20field.)
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