Abstract Channel-Spatial mixed attention methods have been proven to enhance the representation capability of convolutional neural networks. Most existing channel-spatial mixed attention methods decouple the channel domain and spatial domain, investing different orders of computational complexity in calculating attention for the two domains. However, both the channel domain and spatial domain are equally important and deserve comparable levels of complexity in attention computation. To solve this problem, we propose a novel channel-spatial mixed attention method, which is called D ual-domain B alanced C hannel- S patial M ixed A ttention (DBCSMA). The input tensor is first decomposed into channel and spatial domains using global pooling. Then, the same order of complexity is dedicated to calculating attention in both the channel domain and the spatial domain, and a mixed attention tensor is generated using multiplicative aggregation. Finally, input channel information and spatial information are simultaneously recalibrated using multiplication of this mixed attention tensor and the input tensor. Experimental results demonstrate that DBCSMA achieves superior performance compared to most existing channel-spatial mixed attention methods while using lower complexity.

Existing unsupervised person re-identification approaches fail to fully capture the fine-grained features of local regions, which can result in people with similar appearances and different identities being assigned the same label after clustering. The identity-independent information contained in different local regions leads to different levels of local noise. To address these challenges, joint training with local soft attention and dual cross-neighbor label smoothing (DCLS) is proposed in this study. First, the joint training is divided into global and local parts, whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions, which improves the ability of the re-identification model in identifying a person’s local significant features. Second, DCLS is designed to progressively mitigate label noise in different local regions. The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions, thereby achieving label smoothing of the global and local regions throughout the training process. In extensive experiments, the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.

Recently, solving the crowd counting problem under occlusion and complex perspective is a hot but difficult topic. Existing methods mainly constructed counters in parallel perspective, but when facing complex perspective, such as the influences of height difference and heavy occlusions, they fail to get good accuracy. To alleviate these problems, this work proposes a novel and interesting framework NOOMP (Need Only One More Point) for perspective adaptation crowd counting task in complex nature scenes. Firstly, this work considers that the common scenes in our daily life usually have the height difference, which brings complex perspective to crowd counting. So, a new labeled method, Absolute-geometry Gaussian Generation is proposed, which only needs one more point for each person in image and gets better accuracy. Secondly, the NOOMP framework consists of meta-learning structure and uses the few-shot way to train the counting model, which can implement the perspective adaptation effective and solve the problem of high label cost. Thirdly, for fitting the characteristic of few-shot learning, this work proposes a new Multi-head Parallel Network (MPNet) for NOOMP. The feature of crowd is extracted by MPNet, which is a hybrid structure composed of shallow network and deep network. This network can save the features of shallow network and the deeper network effectively, which makes MPNet performs well in NOOMP. In addition, this work collects a new dataset, named Multiple Height Differences in Mall (MHDM) for NOOMP, which contains images of different views and height differences from shopping malls and supermarkets. Experiments based on MHDM and other benchmarks show that the NOOMP has good performances in model accuracy and works well for solving perspective change problem.