Detecting Twenty-thousand Classes using Image-level Supervision
This addresses the problem of limited object detection vocabulary for researchers and practitioners, offering a simpler and more effective method for open-vocabulary and long-tail detection, though it is incremental in improving existing detection frameworks.
The paper tackles the limitation of object detectors in vocabulary size by proposing Detic, which trains detector classifiers on image classification data to expand vocabulary to tens of thousands of concepts, resulting in gains such as 2.4 mAP for all classes and 8.3 mAP for novel classes on the LVIS benchmark.
Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as their datasets are larger and easier to collect. We propose Detic, which simply trains the classifiers of a detector on image classification data and thus expands the vocabulary of detectors to tens of thousands of concepts. Unlike prior work, Detic does not need complex assignment schemes to assign image labels to boxes based on model predictions, making it much easier to implement and compatible with a range of detection architectures and backbones. Our results show that Detic yields excellent detectors even for classes without box annotations. It outperforms prior work on both open-vocabulary and long-tail detection benchmarks. Detic provides a gain of 2.4 mAP for all classes and 8.3 mAP for novel classes on the open-vocabulary LVIS benchmark. On the standard LVIS benchmark, Detic obtains 41.7 mAP when evaluated on all classes, or only rare classes, hence closing the gap in performance for object categories with few samples. For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without finetuning. Code is available at \url{https://github.com/facebookresearch/Detic}.