Mutually-Aware Feature Learning for Few-Shot Object Counting
This work improves few-shot object counting for practical applications by reducing confusion when multiple object classes are present, though it is incremental as it builds on existing extract-and-match approaches.
The paper tackles the problem of few-shot object counting by addressing the lack of interaction between query and exemplar features in existing methods, which leads to target confusion in multi-category scenarios. The proposed MAFEA framework achieves state-of-the-art performance on FSCD-LVIS and FSC-147 benchmarks with reduced target confusion.
Few-shot object counting has garnered significant attention for its practicality as it aims to count target objects in a query image based on given exemplars without additional training. However, the prevailing extract-and-match approach has a shortcoming: query and exemplar features lack interaction during feature extraction since they are extracted independently and later correlated based on similarity. This can lead to insufficient target awareness and confusion in identifying the actual target when multiple class objects coexist. To address this, we propose a novel framework, Mutually-Aware FEAture learning (MAFEA), which encodes query and exemplar features with mutual awareness from the outset. By encouraging interaction throughout the pipeline, we obtain target-aware features robust to a multi-category scenario. Furthermore, we introduce background token to effectively associate the query's target region with exemplars and decouple its background region. Our extensive experiments demonstrate that our model achieves state-of-the-art performance on FSCD-LVIS and FSC-147 benchmarks with remarkably reduced target confusion.