Aggregating Bilateral Attention for Few-Shot Instance Localization

Attention filtering under various learning scenarios has proven advantageous in enhancing the performance of many neural network architectures. The mainstream attention mechanism is established upon the non-local block, also known as an essential component of the prominent Transformer networks, to catch long-range correlations. However, such unilateral attention is often hampered by sparse and obscure responses, revealing insufficient dependencies across images/patches, and high computational cost, especially for those employing the multi-head design. To overcome these issues, we introduce a novel mechanism of aggregating bilateral attention (ABA) and validate its usefulness in tackling the task of few-shot instance localization, reflecting the underlying query-support dependency. Specifically, our method facilitates uncovering informative features via assessing: i) an embedding norm for exploring the semantically-related cues; ii) context awareness for correlating the query data and support regions. ABA is then carried out by integrating the affinity relations derived from the two measurements to serve as a lightweight but effective query-support attention mechanism with high localization recall. We evaluate ABA on two localization tasks, namely, few-shot action localization and one-shot object detection. Extensive experiments demonstrate that the proposed ABA achieves superior performances over existing methods.