Abstract
Few-shot object detection (FSOD) is a vital and challenging task in which the aim is to detect unseen object classes with a few annotated samples. However, the discriminative semantic information existing in the new category is not well represented in most existing approaches. To address this issue, we propose a new few-shot object detection model named TMD-FS with Transformer multi-model directing, where the lost discriminative information is mined by adapting multi-modal semantic alignment. Specifically, we transfer the multi-model information into a mixed sequence and map the visual and semantic information into the embedding space. Moreover, we propose a Semantic Visual Transformer (SVT) module to incorporate and align the visual and semantic embedding. Finally, the distance in terms of the visual and semantic embedding is minimized on the basis of the attention loss. Experimental results demonstrate that the performance of the model significantly with few samples. In addition, it achieves state-of-the-art performance when the amount of samples increases.
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Acknowledgement
The research is partially sponsored by The Beijing Municipal Education Commission Project (No. KZ201910005008), The National Natural Science Foundation of China (No.62176009).
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Yuan, Y., Duan, L., Wang, W., En, Q. (2021). TMD-FS: Improving Few-Shot Object Detection with Transformer Multi-modal Directing. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_37
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