ECA-Net clearly outperforms SENet, and also outperforms fixed kernel version of ECA-Net. ECA-Net is superior to SENet and CBAM while it is very competitive to AA-Net with lower model complexity. Note that AA-Net is trained with Inception data augmentation and different setting of learning rates. ECA-Net performs favorably against state-of-the-art CNNs while benefiting much lower model complexity. Different frameworks are used, ECA-Net can well generalize to object detection task.

Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines pseudo-labels estimated via a self-training scheme by imposing multi-camera constraints. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. In addition, we develop a method that appropriately selects a person from the pair that contributes to the performance improvement. We evaluate the performance of the network using public and private datasets and confirm the performance surpasses state-of-the-art methods in domains with overlapping camera views. To the best of our knowledge, this is the first study on domain-adaptive learning with multi-camera constraints that can be obtained in real environments.

目前在電腦視覺的領域上，具備深度的卷積神經網路已經在多種任務上取得很大的進步。近年在 Convolution block 的設計上，有發展出一系列相當有潛力的研究，主要是探討將 Attention 機制應用於 Channel 維度的作法。 其中相當具代表性的研究是 SENet，後續也有一些研究嘗試利用更複雜的機制捕捉 Channel-wise dependencies ，或結合 Spatial…