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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 ofthe whole image. To address these tasks, we propose X-DETR, whose architecturehas three major components: an object detector, a language encoder, andvision-language alignment. The vision and language streams are independentuntil 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 optimizedfor the vision-language tasks instead of an off-the-shelf component. Toovercome the limited size of paired object-language annotations, we leverageother weak types of supervision to expand the knowledge coverage. This simpleyet effective architecture of X-DETR shows good accuracy and fast speeds formultiple instance-wise vision-language tasks, e.g., 16.4 AP on LVIS detectionof 1.2K categories at ~20 frames per second without using any LVIS annotationduring training.

X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks