  We introduce MQ-Det, an efficient architecture and pre-training strategy
design to utilize both textual description with open-set generalization and
visual exemplars with rich description granularity as category queries, namely,
Multi-modal Queried object Detection, for real-world detection with both
open-vocabulary categories and various granularity. MQ-Det incorporates vision
queries into existing well-established language-queried-only detectors. A
plug-and-play gated class-scalable perceiver module upon the frozen detector is
proposed to augment category text with class-wise visual information. To
address the learning inertia problem brought by the frozen detector, a vision
conditioned masked language prediction strategy is proposed. MQ-Det's simple
yet effective architecture and training strategy design is compatible with most
language-queried object detectors, thus yielding versatile applications.
Experimental results demonstrate that multi-modal queries largely boost
open-world detection. For instance, MQ-Det significantly improves the
state-of-the-art open-set detector GLIP by +7.8% AP on the LVIS benchmark via
multi-modal queries without any downstream finetuning, and averagely +6.3% AP
on 13 few-shot downstream tasks, with merely additional 3% modulating time
required by GLIP. Code is available at https://github.com/YifanXu74/MQ-Det.
