  We present an approach to pose object recognition as next token prediction.
The idea is to apply a language decoder that auto-regressively predicts the
text tokens from image embeddings to form labels. To ground this prediction
process in auto-regression, we customize a non-causal attention mask for the
decoder, incorporating two key features: modeling tokens from different labels
to be independent, and treating image tokens as a prefix. This masking
mechanism inspires an efficient method - one-shot sampling - to simultaneously
sample tokens of multiple labels in parallel and rank generated labels by their
probabilities during inference. To further enhance the efficiency, we propose a
simple strategy to construct a compact decoder by simply discarding the
intermediate blocks of a pretrained language model. This approach yields a
decoder that matches the full model's performance while being notably more
efficient. The code is available at https://github.com/kaiyuyue/nxtp
