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Cross-domain Detection Transformer based on Spatial-aware and Semantic-aware Token Alignment | | Jinhong Deng
; Xiaoyue Zhang
; Wen Li
; Lixin Duan
; | | Date: |
1 Jun 2022 | | Abstract: | Detection transformers like DETR have recently shown promising performance on
many object detection tasks, but the generalization ability of those methods is
still quite challenging for cross-domain adaptation scenarios. To address the
cross-domain issue, a straightforward way is to perform token alignment with
adversarial training in transformers. However, its performance is often
unsatisfactory as the tokens in detection transformers are quite diverse and
represent different spatial and semantic information. In this paper, we propose
a new method called Spatial-aware and Semantic-aware Token Alignment (SSTA) for
cross-domain detection transformers. In particular, we take advantage of the
characteristics of cross-attention as used in detection transformer and propose
the spatial-aware token alignment (SpaTA) and the semantic-aware token
alignment (SemTA) strategies to guide the token alignment across domains. For
spatial-aware token alignment, we can extract the information from the
cross-attention map (CAM) to align the distribution of tokens according to
their attention to object queries. For semantic-aware token alignment, we
inject the category information into the cross-attention map and construct
domain embedding to guide the learning of a multi-class discriminator so as to
model the category relationship and achieve category-level token alignment
during the entire adaptation process. We conduct extensive experiments on
several widely-used benchmarks, and the results clearly show the effectiveness
of our proposed method over existing state-of-the-art baselines. | | Source: | arXiv, 2206.00222 | | Services: | Forum | Review | PDF | Favorites | |
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