Transcription of TransReID: Transformer-Based Object Re-Identification
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TransReID: Transformer-Based Object Re-Identification
TransReID: Transformer-Based Object Re-IdentificationShuting He1,2*Hao Luo2 Pichao Wang2 Fan Wang2 Hao Li2 Wei Jiang1 1 Zhejiang University2 Alibaba robust feature representation is one of thekey challenges in Object Re-Identification (ReID). Althoughconvolution neural network (CNN)-based methods haveachieved great success, they only process one localneighborhood at a time and suffer from information loss ondetails caused by convolution and downsampling operators( and strided convolution).To overcomethese limitations, we propose a pure transformer-basedobject ReID framework named TransReID. Specifically,we first encode an image as a sequence of patchesand build a Transformer-Based strong baseline with afew critical improvements, which achieves competitiveresults on several ReID benchmarks with CNN-basedmethods.
Pure Transformer models are becoming more and more popular. For example, Image Processing Transformer (IPT) [2] takes advantage of transformers by using large scale pre-training and achieves the state-of-the-art performance on several image processing tasks like super-resolution, denoising and de-raining. ViT [8] is proposed
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