  The detection of moving infrared dim-small targets has been a challenging and
prevalent research topic. The current state-of-the-art methods are mainly based
on ConvLSTM to aggregate information from adjacent frames to facilitate the
detection of the current frame. However, these methods implicitly utilize
motion information only in the training stage and fail to explicitly explore
motion compensation, resulting in poor performance in the case of a video
sequence including large motion. In this paper, we propose a Deformable Feature
Alignment and Refinement (DFAR) method based on deformable convolution to
explicitly use motion context in both the training and inference stages.
Specifically, a Temporal Deformable Alignment (TDA) module based on the
designed Dilated Convolution Attention Fusion (DCAF) block is developed to
explicitly align the adjacent frames with the current frame at the feature
level. Then, the feature refinement module adaptively fuses the aligned
features and further aggregates useful spatio-temporal information by means of
the proposed Attention-guided Deformable Fusion (AGDF) block. In addition, to
improve the alignment of adjacent frames with the current frame, we extend the
traditional loss function by introducing a new motion compensation loss.
Extensive experimental results demonstrate that the proposed DFAR method
achieves the state-of-the-art performance on two benchmark datasets including
DAUB and IRDST.
