In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To address these tasks, we propose X-DETR, whose architecture has three major components: an object detector, a language encoder, and vision-language alignment. The vision and language streams are independent until the end and they are aligned using an efficient dot-product operation. The whole network is trained end-to-end, such that the detector is optimized for the vision-language tasks instead of an off-the-shelf component. To overcome the limited size of paired object-language annotations, we leverage other weak types of supervision to expand the knowledge coverage. This simple yet effective architecture of X-DETR shows good accuracy and fast speeds for multiple instance-wise vision-language tasks, e.g., 16.4 AP on LVIS detection of 1.2K categories at ~20 frames per second without using any LVIS annotation during training.
@article{arxiv.2204.05626,
title = {X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks},
author = {Zhaowei Cai and Gukyeong Kwon and Avinash Ravichandran and Erhan Bas and Zhuowen Tu and Rahul Bhotika and Stefano Soatto},
journal= {arXiv preprint arXiv:2204.05626},
year = {2022}
}