  Re-identification (ReID) is to identify the same instance across different
cameras. Existing ReID methods mostly utilize alignment-based or
attention-based strategies to generate effective feature representations.
However, most of these methods only extract general feature by employing single
input image itself, overlooking the exploration of relevance between comparing
images. To fill this gap, we propose a novel end-to-end trainable dynamic
convolution framework named Instance and Pair-Aware Dynamic Networks in this
paper. The proposed model is composed of three main branches where a
self-guided dynamic branch is constructed to strengthen instance-specific
features, focusing on every single image. Furthermore, we also design a
mutual-guided dynamic branch to generate pair-aware features for each pair of
images to be compared. Extensive experiments are conducted in order to verify
the effectiveness of our proposed algorithm. We evaluate our algorithm in
several mainstream person and vehicle ReID datasets including CUHK03,
DukeMTMCreID, Market-1501, VeRi776 and VehicleID. In some datasets our
algorithm outperforms state-of-the-art methods and in others, our algorithm
achieves a comparable performance.
