A New Language Interface for Object Detection

 A New Language Interface for Object Detection

Pix2Seq: A New Language Interface for Object Detection  in the Google AI Blog

Friday, April 22, 2022

Posted by Ting Chen and David Fleet, Research Scientists, Google Research, Brain Team

Object detection is a long-standing computer vision task that attempts to recognize and localize all objects of interest in an image. The complexity arises when trying to identify or localize all object instances while also avoiding duplication. Existing approaches, like Faster R-CNN and DETR, are carefully designed and highly customized in the choice of architecture and loss function. This specialization of existing systems has created two major barriers: (1) it adds complexity in tuning and training the different parts of the system (e.g., region proposal network, graph matching with GIOU loss, etc.), and (2), it can reduce the ability of a model to generalize, necessitating a redesign of the model for application to other tasks.

In “Pix2Seq: A Language Modeling Framework for Object Detection”, published at ICLR 2022, we present a simple and generic method that tackles object detection from a completely different perspective. Unlike existing approaches that are task-specific, we cast object detection as a language modeling task conditioned on the observed pixel inputs. We demonstrate that Pix2Seq achieves competitive results on the large-scale object detection COCO dataset compared to existing highly-specialized and well-optimized detection algorithms, and its performance can be further improved by pre-training the model on a larger object detection dataset. To encourage further research in this direction, we are also excited to release to the broader research community Pix2Seq’s code and pre-trained models along with an interactive demo.  ....