Adapting Hierarchical Transformer for Scene-Level Sketch-Based Image Retrieval
Jie Yang, Aihua Ke, Bo Cai · 2023
Sketch-based image retrieval (SBIR) is an essential application of sketches. Research on object-level SBIR is relatively mature, but the study of more complex scene-level SBIR is still in its early stages. In order to advance this research, we investigate previous works and identify two main shortcomings: (1) insufficient utilization of multi-scale features from sketches and images, and (2) lack of effective modules to eliminate the substantial domain gap between them. To address these issues, we propose SketchRetriever, a hierarchical Transformer-based scene-level SBIR model. In our model, the hierarchical Transformer and compressors are capable of efficiently capturing feature maps at various granularities and compressing them into corresponding feature vectors, and the modality-specific Adapters can project the feature embeddings of sketches and images into the same feature space, thereby closing the domain gap between them. We adopt the adapter-tuning strategy, which not only considerably reduces the number of tunable parameters but also effectively avoids overfitting. Extensive experiments demonstrate that SketchRetriever significantly outperforms state-of-the-art methods on two benchmark datasets with lower fine-tuning overhead.