The existing learning-based unsupervised hashing method usually uses a pre-trained network to extract features, and then uses the extracted feature vectors to construct a similarity matrix which guides the generation of hash codes through gradient descent. Existing research shows that the algorithm based on gradient descent will cause the hash codes of the paired images to be updated toward each other’s position during the training process. For unsupervised training, this situation will cause large fluctuations in the hash code during training and limit the learning efficiency of the hash code. In this paper, we propose a method named Deep Unsupervised Hashing with Gradient Attention (UHGA) to solve this problem. UHGA mainly includes the following contents: (1) use pre-trained network models to extract image features; (2) calculate the cosine distance of the corresponding features of the pair of images, and construct a similarity matrix through the cosine distance to guide the generation of hash codes; (3) a gradient attention mechanism is added during the training of the hash code to pay attention to the gradient. Experiments on two existing public datasets show that our proposed method can obtain more discriminating hash codes.
RGB-D salient object detection (SOD) usually describes two modes' classification or regression problem, namely RGB and depth. The existing RGB-D SOD methods use depth hints to increase the detection performance, meanwhile they focus on the quality of little depth maps. In practical application, the interference of various problems in the acquisition process affects the depth map quality, which dramatically reduces the detection effect. In this paper, to minimize interference in depth mapping and emphasize prominent objects in RGB images, we put forward a layered interactive attention network (LIANet). In general, this network consists of three essential parts: feature coding, layered fusion mechanism, and feature decoding. In the feature coding stage, three-dimensional weight is introduced to the features of each layer without adding network parameters, and it is also a lightweight module. The layered fusion mechanism is the most critical part of this paper. RGB and depth maps are used alternately for layered interaction and fusion to enhance RGB feature information and gradually integrate global context information at a single scale. In addition, we also used mixed losses to optimize further and train our model. Finally, a mass of experiments on six standard datasets demonstrated the importance of the method, and a timely detection speed reaches 30 fps on every dataset.
Currently, deep learning is the mainstream method to solve the problem of person reidentification. With the rapid development of neural networks in recent years, a number of neural network frameworks have emerged for it, so it is becoming more important to explore a simple and efficient baseline algorithm. In fact, the performance of the same module varies greatly in different positions of the network architecture. After exploring how modules can play a maximum role in the network and studying and summarizing existing algorithms, we designed an adaptive multiple loss baseline (AML) with a simple structure but powerful functions. In this network, we use an adaptive mining sample loss (AMS) and other modules, which can mine more information from input samples at the same time. Based on triplet loss, AMS loss can optimize the distance between the input sample and its positive and negative samples and protect structural information within the sample. During the experiment, we conducted several group tests and confirmed the high performance of AML baseline via the results. AML baseline has outstanding performance in three commonly used datasets. The two indicators of AML baseline on CUHK-03 are 25.7% and 26.8% higher than BagTricks.
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