ZHANG

Pix2seq: A Language Modeling Framework for Object Detection casts object detection as a language modeling task conditioned on the observed pixel inputs. Object descriptions (e.g., bounding boxes and class labels) are expressed as sequences of discrete tokens, and we train a neural network to perceive the image and generate the desired sequence. Our approach is based mainly on the intuition that if a neural network knows about where and what the objects are, we just need to teach it how to read them out. Experiment results are shown in Table 1, which indicates Pix2seq achieves state of art result on coco.

Primary location: Nottingham United Kingdom