Part based models have been proved to be beneficial for person re-identification (Re-ID) in recent years. Existing models usually use fixed horizontal stripes or rely on human keypoints to get each part, which is not consistent with the human visual mechanism. In this paper, we propose a Self-Channel Attention Weighted Part model (SCAWP) for Re-ID. In SCAWP, we first learn a feature map from ResNet50 and use 1x1 convolution to reduce the dimension of this feature map, which could aggregate the channel information. Then, we learn the weight map of attention within each channel and multiply it with the feature map to get each part. Finally, each part is used for a special identification task to build the whole model. To verify the performance of SCAWP, we conduct experiment on three benchmark datasets, including CUHK03-NP, Market-1501 and DukeMTMC-ReID. SCAWP achieves rank-1/mAP accuracy of 70.4%/68.3%, 94.6%/86.4% and 87.6%/76.8% on three datasets respectively.

Feature learning based on deep network has been verified as beneficial for person re-identification (Re-ID) in recent years. However, most researches use a single network as the baseline, without considering the fusion of different deep features. By analyzing the attention maps of different networks, we find that the information learned by different networks can complement each other. Therefore, a novel Dual Network Fusion (DNF) framework is proposed. DNF is designed with a trunk branch and two auxiliary branches. In the trunk branch, deep features are cascaded directly along the channel direction. One of the auxiliary branch is channel attention branch, which is used to allocate weight for different deep features. Another one is multi-loss training branch. To verify the performance of DNF, we test it on three benchmark datasets, including CUHK03NP, Market-1501 and DukeMTMC-reID. The results show that the effect of using DNF is significantly better than a single network and is comparable to most state-of-the-art methods.

This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.