Towards Data-Efficient Detection Transformers
This addresses the problem of data inefficiency in object detection for researchers and practitioners using transformers, though it is incremental as it builds on existing detection transformer methods.
The paper tackles the data-hungry nature of detection transformers, which suffer performance drops on small datasets like Cityscapes, by modifying cross-attention key/value construction and adding label augmentation, improving performance on both small and large datasets.
Detection Transformers have achieved competitive performance on the sample-rich COCO dataset. However, we show most of them suffer from significant performance drops on small-size datasets, like Cityscapes. In other words, the detection transformers are generally data-hungry. To tackle this problem, we empirically analyze the factors that affect data efficiency, through a step-by-step transition from a data-efficient RCNN variant to the representative DETR. The empirical results suggest that sparse feature sampling from local image areas holds the key. Based on this observation, we alleviate the data-hungry issue of existing detection transformers by simply alternating how key and value sequences are constructed in the cross-attention layer, with minimum modifications to the original models. Besides, we introduce a simple yet effective label augmentation method to provide richer supervision and improve data efficiency. Experiments show that our method can be readily applied to different detection transformers and improve their performance on both small-size and sample-rich datasets. Code will be made publicly available at \url{https://github.com/encounter1997/DE-DETRs}.