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
@article{arxiv.2312.02142,
title = {Object Recognition as Next Token Prediction},
author = {Kaiyu Yue and Bor-Chun Chen and Jonas Geiping and Hengduo Li and Tom Goldstein and Ser-Nam Lim},
journal= {arXiv preprint arXiv:2312.02142},
year = {2024}
}