“Object Recognition As Next Token Prediction”, Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim2023-12-04 (GPT)⁠:

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.