1 research outputs found
Efficient Multi-Scale Attention Module with Cross-Spatial Learning
Remarkable effectiveness of the channel or spatial attention mechanisms for
producing more discernible feature representation are illustrated in various
computer vision tasks. However, modeling the cross-channel relationships with
channel dimensionality reduction may bring side effect in extracting deep
visual representations. In this paper, a novel efficient multi-scale attention
(EMA) module is proposed. Focusing on retaining the information on per channel
and decreasing the computational overhead, we reshape the partly channels into
the batch dimensions and group the channel dimensions into multiple
sub-features which make the spatial semantic features well-distributed inside
each feature group. Specifically, apart from encoding the global information to
re-calibrate the channel-wise weight in each parallel branch, the output
features of the two parallel branches are further aggregated by a
cross-dimension interaction for capturing pixel-level pairwise relationship. We
conduct extensive ablation studies and experiments on image classification and
object detection tasks with popular benchmarks (e.g., CIFAR-100, ImageNet-1k,
MS COCO and VisDrone2019) for evaluating its performance.Comment: Accepted to ICASSP202